Horizon 2020 Marie Skłodowska-Curie Innovative Training Network

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Contributions to Workshops and Conferences 2018-12-07T12:05:21+00:00

 About the overall TRUSS ITN project 

Inspections and maintenance of infrastructure are expensive. In some cases, overdue or insufficient maintenance/monitoring can lead to an unacceptable risk of collapse and to a tragic failure as the Morandi bridge in Genoa, Italy, on 14th August 2018. An accurate assessment of the safety of a structure is a difficult task due to uncertainties associated with the aging and response of the structure, with the operational and environmental loads, and with their interaction. During the period from 2015 to 2019, the project TRUSS (Training in Reducing Uncertainty in Structural Safety) ITN (Innovative Training Network), funded by the EU H2020 Marie Curie-Skłodowska Action (MSCA) programme, has worked towards improving the structural assessment of buildings, energy, marine, and transport infrastructure. Fourteen Early Stage Researchers (ESRs) have been recruited to carry out related research on new materials, testing methods, improved and more efficient modelling methods and management strategies, and sensor and algorithm development for Structural Health Monitoring (SHM) purposes. This research has been enhanced by an advanced program of scientific and professional training delivered via a collaboration between 6 Universities, 1 research institute and 11 companies from 5 European countries. The high proportion of companies participating in TRUSS ITN has ensured significant industrial expertise and has introduced a diverse range of perspectives to the consortium on the activities necessary to do business in the structural safety sector. -> Link to full text in repository
The structural deterioration of aging structures is often aggravated by an increase in loads that was not foreseen at the design stage and an insufficient maintenance spending as a result of the economic downturn of recent years. A management strategy guaranteeing structural safety with the best use of the resources available is clearly needed. TRUSS (Training in Reducing Uncertainty in Structural Safety, http://trussitn.eu/, 2015-2019) is a €3.7 million Marie Skłodowska-Curie innovative training network funded by the European Horizon 2020 Research and Innovation Programme, with the main objectives of: (1) carrying out research that will ensure structural safety levels for buildings, energy and transport infrastructure, and (2) providing training to a new generation of researchers for dealing with an aging infrastructure stock. The network is composed by 6 Universities, 11 companies and 1 research institute from 5 European countries, joining forces to identify, quantify and reduce uncertainties associated to the structural response, to the imposed loads, and to the probability of structural failure. -> Link to full text in repository
TRUSS (Training in Reducing Uncertainty in Structural Safety, http://trussitn.eu, 2015-2019) Innovative Training Network (ITN) is a €3.7 million Marie Skłodowska-Curie Action funded by the European Union under the Horizon 2020 Programme (European Commission 2014). TRUSS develops tools for reducing uncertainty in structural safety and improving infrastructure management, as well as laying the basis for an advanced doctoral programme that will qualify 14 Early Stage Researchers (ESRs) for dealing with the challenges of an aging European infrastructure stock, thereby meeting a critical need whilst at the same time enhancing their career prospects, whether in industry or academia (González 2017). The network is composed of 6 Universities, 11 Industry participants and 1 research institute from 5 European countries (Denmark, France, Ireland, Spain and UK), that share expertise and provide supervision, training and secondment opportunities in different environments enhancing the research by the ESRs. In the field of short and medium span bridges, TRUSS develops sensors and algorithms that will allow a more efficient monitoring of the bridge stock and a prioritization of the measures necessary to ensure structural integrity and safety. In particular, the following concepts are investigated (González et al 2017): (i) the sensitivity of rotation to damage when a bridge is traversed by a moving load, (ii) the effect of localized damage on the global bridge safety via Bayesian updating, (iii)  the ability of a Bayesian Belief Network to predict the health state of a bridge, (iv) the sensitive of dynamic features of the bridge response to damage, (v) the spatial resolution, strain accuracy and long-term reliability of measurements performed with optical fibre distributed sensing, (vi) the feasibility of employing sensors mounted on an instrumented vehicle to detect damage while traversing a bridge, and (vii) the use of an unmanned aerial vehicle for image-based inspection.

References:

European Commission. 2014. Horizon 2020 Work Programme 2014-2015 Marie Skłodowska-Curie Actions. European Commission Decision C. 4995 of 22 July 2014.

González, A. 2017. Developments in damage assessment by Marie Skłodowska-Curie TRUSS ITN project. Journal of Physics: Conf. Series, IOPscience, 842(1): 012039.

González, A., Huseynov, F., Heitner, B., Vagnoli, M., Moughty, J.J., Barrias, A., Martinez, D., Chen, S., OBrien, E., Laefer, D., Casas, J.R., Remenyte-Prescott, R., Yalamas, T. and Brownjohn, J. 2017. Structural health monitoring developments in TRUSS Marie Skłodowska-Curie innovative training network. Proceedings of 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-8), Brisbane, Australia, December 5-8.

The performance of four methods in establishing whether a bridge has experienced damage or not from the response to the crossing of a vehicle, is assessed within the Marie Skłodowska-Curie Innovative Training Net-work titled TRUSS (Training in Reducing Uncertainty of Structural Safety, http://trussitn.eu, 2015-2019). The first and second methods extract vibration-based damage sensitive features and apply a Bayesian Belief net-work respectively from/to bridge accelerations to determine the occurrence of damage. A third method based on bridge rotations applies bridge weigh-in-motion concepts to detect damage from the estimated vehicle’s weights. Finally, a fourth method derives bridge curvature and damage from the vehicle response. A blind test based on the responses of a complex vehicle-bridge interaction theoretical model is carried out to assess performance. The estimations of damage by each method are collected independently and compared to the exact solution.
While an efficient infrastructure asset provides economic and social benefits, infrastructure failure in terms of capacity or reliability can involve significant economic costs, lower quality of life and most importantly, human losses. Failure can be motivated by an accelerated ageing due to an increasing demand in operational and environmental loads. This deterioration has often been aggravated by the insufficient maintenance spending as a result of the economic and  financial crisis. Therefore, a management strategy guaranteeing proper maintenance and structural safety with the best use of the resources available is needed. TRUSS (Training in Reducing Uncertainty in Structural Safety, http://trussitn.eu/, 2015-2019) is a €3.7 million Marie Skłodowska-Curie innovative training network funded by European Union’s Horizon 2020 research and innovation programme, with the main objective of ensuring structural safety levels for buildings, energy and transport infrastructure. The network is composed of 6 Universities, 11 companies and 1 research institute from 5 European countries. Complementary training and secondment opportunities by the network allow 14 fellows to be exposed to technologies evolving rapidly, and to research and innovation in both academia and industry. Individual projects fall within two research clusters: (i) buildings, energy (nuclear, wind turbine towers) and marine (off-shore, ships and ships unloaders) infrastructure, and (ii) rail and road infrastructure (pavement, railway and highway bridges). Some fellows are focused on lab tests to reduce uncertainty associated to measurement techniques and to materials. Other fellows are concerned with the development of efficient structural monitoring systems (i.e., based on distributed optical-fiber sensing, instrumented drones or land vehicles). A third group of fellows is developing algorithms for damage detection, estimation of remaining life and structural inspection purposes. The improved reliability resulting from TRUSS is expected to contribute to more efficient designs and maintenance strategies and to the cancellation of costly and unnecessary interventions in existing structures.
There is multitude of models available to assess structural safety based on a set of input parameters. As the degree of complexity of the models increases, the uncertainty of their output tends to decrease. However, more complex models typically require more input parameters, which may contain a higher degree of uncertainty. Therefore, it becomes necessary to find the balance that, for a particular scenario, will reduce the overall uncertainty (model + parameters) in structural safety. The latter is the objective of the Marie Skłodowska-Curie Innovative Training Network titled TRUSS (Training in Reducing Uncertainty in Structural Safety) funded by the EU Horizon 2020 research and innovation programme (http://trussitn.eu). This paper describes how TRUSS addresses uncertainty in: (a) structural reliability of materials such as basalt fiber reinforced polymer, (b) testing techniques in the assessment of concrete strength in buildings, (c) numerical methods in computing the non-linear response of submerged nuclear components subjected to an earthquake, (d) estimation of life of wind turbines, (e) the optimal inspection times and management strategies for ships, (f) characterization of the dynamic response of ship unloaders and (g) the relationship between vehicles fuel consumption and pavement condition.-> Link to full text in repository
This paper reports on recent contributions by the Marie Skłodowska-Curie Innovative Training Network titled TRUSS (Training in Reducing Uncertainty of Structural Safety) to the field of structural safety in rail and road bridges (http://trussitn.eu). In TRUSS, uncertainty in bridge safety is addressed via cost efficient structural performance monitoring and fault diagnostics methods including: (1) the use of the rotation response due to the traffic traversing a bridge and weigh-in-motion concepts as damage indicator, (2) the combination of design parameters in probabilistic context for geometrical and material properties, traffic data and assumption on level of deterioration to evaluate bridge safety (via Bayesian updating and a damage indicator based on real time measurement), (3) the application of a fuzzy classification technique via feature selection extracted using empirical mode decomposition to detect failure, and (4) the testing of alternative vibration based damage sensitive features other than modal parameters. Progress has also been made in improving modern technologies based on optical fiber distributed sensing, and sensors mounted on instrumented terrestrial and on aerial vehicles, in order to gather more accurate and efficient info about the structure. More specifically, the following aspects have been covered: (a) the spatial resolution and strain accuracy obtained with optical distributed fiber when applied to concrete elements as well as the ideal adhesive, and the potential for detecting crack or abnormal deflections without failure or debonding, (b) the possibility of using the high-resolution measurement capabilities of the Traffic Speed Deflectometer for bridge monitoring purposes and, (c) the acquisition of bridge details and defects via unmanned aerial vehicles. -> Link to full text in repository
The growth of cities, the impacts of climate change and the massive cost of providing new infrastructure provide the impetus for TRUSS (Training in Reducing Uncertainty in Structural Safety), a €3.7 million Marie Skłodowska-Curie Action Innovative Training Network project funded by EU’s Horizon 2020 programme, which aims to maximize the potential of infrastructure that already exists (http://trussitn.eu). For that purpose, TRUSS brings together an international, inter-sectoral and multidisciplinary collaboration between five academic and eleven industry institutions from five European countries. The project covers rail and road infrastructure, buildings and energy and marine infrastructure. This paper reports progress in fields such as advanced sensor-based structural health monitoring solutions – unmanned aerial vehicles, optical backscatter reflectometry, monitoring sensors mounted on vehicles, … – and innovative algorithms for structural designs and short- and long-term assessments of buildings, bridges, pavements, ships, ship unloaders, nuclear components and wind turbine towers that will support infrastructure operators and owners in managing their assets. [DOI] -> Link to full text in repository

About WP4. Buildings, Energy and Marine Infrastructure

This study investigates the tensile behaviour of basalt fibre reinforced polymer (BFRP) composites that were developed using braiding as a manufacturing technique. Those materials will be introduced in concrete reinforcement applications. Three BFRP rebar sizes with a circular constant cross section and different braided configurations are developed and characterised with respect to their internal architecture. The braid angle on each layer of the rebar, varying from 10◦ to 45◦, is an important parameter that has a direct impact on its performance characteristics. The effective longitudinal in-plane modulus (ExFRP) of each braided sample is calculated numerically using the classical laminate theory (CLT) approach and then, tensile tests are performed according to the relevant standard. Comparisons between analytical and experimental data demonstrate a significant influence of braiding parameters, like braiding angle and number of braiding layers, on the mechanical properties of BFRP rebars. In addition, it is noteworthy that all predicted moduli determined with CLT numerical approach are found to be higher than the test results and overestimate rebar’s stiffness, most probably due to the degree of undulation from braiding process.
This study compares the physical properties and tensile behaviour of two different basalt fibre reinforced polymer (BFRP) rebar designs. Both types are developed using basalt fibres and epoxy resin as reinforcement and matrix respectively; composites with a constant cross section of 8 mm diameter are manufactured using a vacuum assisted resin infusion technique. The first configuration consists of eight braided layers at various angles, while the second one combines a unidirectional core with four outer braided layers. The latter hybrid design is introduced to improve the elastic modulus of braided BFRP reinforcement used in concrete structures. Tensile performance of all BFRP rebars produced in UCD laboratory is numerically and experimentally evaluated, and results for both approaches are compared. The effective longitudinal in-plane modulus and the fibre volume fractions (φf) of each sample is calculated using the classical laminate theory and then, tensile tests are performed in accordance to the B2_ACI 440.3R-04 standard to experimentally validate the numerical results. Initial findings indicate that the elastic modulus of BFRP rebar can be enhanced by combining braiding with a unidirectional fibre core while a sufficient tensile strength is obtained, but additional research towards an optimal hybrid design is required. -> Link to full text in repository
Basalt Fibre Reinforced Polymer composites were recently introduced as a possible replacement to steel in civil engineering applications due to both their excellent corrosion resistance and their high strength-­to-­weight ratio. These materials also have the potential to provide long-­term durability, while minimising maintenance costs. However, limited data are available in the literature regarding their fatigue performance, and most of them are focused either on their static behaviour or on aerospace applications rather than structural ones. The aim of this research is to experimentally evaluate the mechanical properties, and more specifically, the tensile fatigue performance of braided Basalt Fiber Reinforced Polymer (BFRP) reinforcement. Three different rebar designs with a 5, 8 and 10 mm diameter are manufactured, using braiding and a vacuum assisted resin infusion technique. All types are developed using basalt fibres and epoxy resin as reinforcement and matrix respectively. Tensile fatigue tests on BFRP samples are performed using Instron 500 Universal Testing Machine in accordance to B7_ACI 440.3R-­04 standard. All samples are tested under a fixed load ratio of 0.1 and a loading frequency of 4 Hz. The minimum and maximum load vary accordingly, with the latter ranging from 20 to 60% of the quasi-­static tensile strength. Throughout the whole duration of the fatigue cycling test, the applied load, displacement and specimen elongation are electronically recorded every 10th cycle. The number of repeated loading cycles to failure and stress applied is then used to generate S-­N curves for each sample. A reference specimen of each type is also used for the evaluation of the static tensile performance following the B2_ACI 440.3R-­04 standard. Initial results confirmed a sufficient fatigue performance of braided BFRP rebars with a high stiffness retention at low fatigue loads and good damping properties. Moreover, composites with a lower fiber volume fraction and a higher void content seem to exhibit an increased fatigue stress sensitivity with a reduction of the fatigue limit at elevated fatigue cycles. 
This paper focuses on the use of micro computed tomography (CT) methods for fibre composite material analysis and evaluation of braided Basalt Fiber Reinforced Polymer (BFRP) reinforcement. More specifically, a combined experimental and computational study towards a complete 3D geometrical model of braided BFRP rebars is presented here. Rebars are designed using basalt fibres and epoxy resin as reinforcement and matrix respectively;; composites are developed in three different sizes and configurations, using braiding and a vacuum assisted resin infusion technique. All samples are scanned using micro-­CT imaging and their microstructure is assessed. Geometrical consistency is validated, including measurements on layer thickness, braiding angle and fibre orientation;; yarn cross section deviations from the idealized elliptical shape along the yarn path and nesting effects were observed. Defect and void development, yarn damage and calculation of fibre volume fractions, is completed with VG Studio Max image processing software. In addition, realistic geometrical modelling of braided composites based on these measurements is carried out using TexGen software, towards simulation of their mechanical response with FEA methods.
In recent years the long term durability of reinforced concrete structures has become a major concern. The effect of harsh loading conditions and aggressive environmental factors can lead to corrosion of reinforcing steel in civil engineering applications. This in turn leads to undesired repairs, additional costs and shorter service lives. Advanced composite materials, such as Basalt Fibre Reinforced Polymer (BFRP), have the capacity to significantly address this problem. These materials have enhanced physical properties such as higher mechanical and corrosion resistance, and have the potential to replace traditional steel rebars as tension reinforcement in concrete. There are however limitations that prevent their use on a larger scale, and lack of ductility is the most significant. Braiding techniques could provide the required performance benefits related to the additional ductility and flexibility needed, as well as enhancing the bond between FRP and concrete. If this is achieved, it has the potential to prevent a brittle failure and successfully meet strength, reliability and cost demands. This study focuses on the basics of materials characterization and reliability analysis of internal BFRP reinforcement for concrete structures towards design optimization for structural reliability over their service life. [DOI] -> Link to full text in repository
In recent years, degradation of reinforced concrete (RC) structures due to corrosion of reinforcing steel has become a major concern worldwide. This affects long-term durability, total service life and structural safety of RC elements. Advanced composite materials, such as Basalt Fibre Reinforced Polymer (BFRP), are currently being developed and are showing promising results as a viable alternative to steel in infrastructure applications. More specifically, these materials can offer significant advantages related to both their non-corrodible nature and their enhanced physical and mechanical properties. However, their brittle nature is considered as the main limitation preventing their use on a larger scale. A detailed investigation of manufacturing technologies and design methodologies for the optimum development of BFRP composites, indicates that braiding methods could provide the required performance benefits through increased ductility and flexibility; it can also enhance the bond between FRP and concrete.

This study focuses on exploring the potential of braided Basalt Fibre Reinforced Polymer reinforcement through design optimisation and evaluation of their structural performance. Braided BFRP preforms with different configurations were produced changing key braiding parameters in order to achieve the desired structural geometry and meet the performance characteristics of existing rebar reinforcement. Following from that, successful epoxy resin impregnation trials in regular and spiral configurations confirmed the possibility of manufacturing braided BFRP composites in complex shapes. Moreover, a theoretical numerical approach based on Classical Laminate Theory (CLT) has been developed to determine the stiffness properties of manufactured braided composites, calculating the effective longitudinal in-plane modulus of each braided sample. The relation between geometrical factors and processing conditions on the physical and mechanical properties of the braided rebars was clearly observed. Future plans include assessment of the manufacturing process for improved rebar design, advanced material analysis and characterization tests combined with experimental validation of the developed numerical approach. In addition, finite element analysis (FEA) models will be developed for braided BFRP composites in order to assess the relation between braiding parameters and rebar performance.

In recent years, the development and use of Fibre Reinforced Polymer composite materials in infrastructure have gained increasing attention worldwide. More specifically, natural mineral fibres such as basalt are currently being developed and are showing promising properties. Within an appropriate polymer matrix, their use as reinforcement in concrete structures offers performance benefits related to their environmentally friendly and non-corrodible nature. In particular, BFRPs have the potential to replace conventional internal steel rebar and thus, to be the next generation material in concrete reinforcement applications. A detailed literature review indicates that a careful selection of the appropriate manufacture technique and design methodology are required in order to prevent brittle failure on a concrete structure reinforced with FRP composite material. This paper reports on how to use the additional helical reinforcement and the braid configuration in order to increase strength, structural ductility and long term durability. Moreover, this study outlines the development of an analytical numerical model to predict the longitudinal elastic modulus of braided composites, as well as its validation by comparison of the results with available data from the literature. -> Link to full text in repository
In recent years, significant research has been conducted, by both industry and academia, into the optimum development and use of Fiber Reinforced Polymer composite materials in infrastructure. In particular, it is widely recognised that FRPs have the potential to replace conventional internal steel rebars in concrete reinforcement and offer performance benefits related to their advanced properties, such as corrosion resistance, high tensile strength etc. A review of the available literature indicates that brittle behaviour of FRP can significantly decrease the expected ultimate load capacity and, thus have a negative effect on structure’s long term durability. However, selecting braiding as manufacture technique and enhancing flexural capacity and shear strength through additional helical reinforcement, could provide structure with the additional ductility needed to prevent a brittle failure. Furthermore, the impact of deterioration mechanisms, focusing on the interaction between FRP and concrete in a structure, is an aspect for further investigation via laboratory testing and advanced analysis. This study summarises the results of research on structural design and manufacture methods of FRP composite materials by presenting new configuration and types of FRP reinforcement in order to encourage the use of these promising materials in construction industry. -> Link to full text in repository
In the structural evaluation of existing concrete structures, concrete strength is an important parameter that influences the quality of the overall assessment. Non-destructive tests (NDTs) allows the inspection of larger areas of concrete at lesser cost and time than coring and provides more reliable information than visual inspection. The low reliability of common NDTs in the assessment of compressive strength of concrete limits the use of NDTs in the practical field. A new technique, post-installed screw pullout (PSP) test, based on the modified pullout of post-installed screw, is presented in this paper. The screw transfers the load to the concrete through bearing on the threads. During the complete pullout failure mode, the failure pattern involves local crushing of concrete under the threads. The PSP test was investigated in mortar and concrete to study different factors; compressive strength, presence of aggregates, and the types of aggregate. Mortar was considered to be a homogenous material and thus taken as a baseline for comparing the effect of aggregate type. Experimental studies showed that aggregates play a significant role in the assessment of compressive strength by PSP test, and a better correlation with compressive strength was observed when concretes with different aggregates were analysed separately. In the strength assessment, the degree of variability of the PSP test in terms of R-squared value, standard deviation, coefficient of variation, and RMSE for mortar and concrete with brick chips and lightweight aggregates was found to be low; however concrete with limestone aggregate showed higher variability in the test results. The study confirms that the PSP test is a viable test method with the potential to be reliable and reasonably accurate, yet cost effective; it can also contribute to the reduction of the uncertainty in the assessment of compressive strength of in-situ concrete.
The assessment of concrete compressive strength is an essential element in assessing the load carrying capacity of structural members in an existing structure. The reliability of non-destructive tests (NDTs) results for assessing concrete strength is always a questionable issue. This is mainly due to the uncertainty associated with the strength predictions based on the NDT measurements. This paper studies the Post-installed Screw Pullout (PSP) test as a potential method for assessing in-situ concrete strength. The objective of this paper is to study the reliability of the assessment using PSP test by analysing the effects of several influencing factors: presence of coarse aggregates, types and size of coarse aggregates, and the amount of coarse and fine aggregates. Analyses of results are presented to evaluate the repeatability and reliability of the PSP test with respect to test standard deviation, coefficient of variation and RMSE. The repeatability of the screw pullout test has been compared with the other NDTs available in literature.

For capacity evaluation, the structural assessment of existing structures is necessary. Concrete strength is an important parameter for such assessment. Non-destructive tests (NDTs) are used along with the traditional approach of core testing for strength assessment of concrete in existing structures. The low reliability of NDT results leads to uncertainty in assessing concrete strength. A new method of non-destructive testing is presented in this paper with the aim of achieving better reliability and reducing uncertainty in the assessment of mortar strength. This approach is based on a modified pullout of post-installed screw anchors. The technique involves a pushing mechanism for a steel screw inside the mortar where a void underneath the screw is left to allow for the uninterrupted movement of the screw inside the concrete. The failure pattern involves local crushing of concrete between the threads of the screw. This paper investigates the load bearing behavior of threaded screws installed in cement mortar under compressive loading. The results supports the application of the technique in the assessment of the compressive strength of mortar. The main parameters affecting the pushing behavior are presented and their effects are discussed. It is planned to extend the test program to concrete in the future. -> Link to full text in repository

With more emphasis on reusing and extending the life of structures, it often becomes necessary to assess the capacity of existing concrete structures. One major component of this assessment relates to the concrete strength. Ideally such assessment is carried out without damaging the concrete of the structure. The currently available methods for assessing in-situ concrete strength as a part of capacity evaluation of the existing structures can be broadly divided into two groups. One group of tests is completely non-destructive. The other group is partially destructive where limited damage to the surface is caused by the tests. For the strength evaluation of existing concrete, methods such as surface hardness test, ultrasonic pulse velocity test, penetration resistance test and maturity test fall under the non-destructive category. Partially destructive tests include pull out test, CAPO test, pull off test and break off test. This paper critically evaluates and analyses the applicability and limitations of the methods used for evaluating concrete strength in existing structures. Most methods for strength evaluation are found to measure a certain property such as elasticity, density, tensile strength or hardness of concrete and then relate the measured value to compressive strength. Studies on these methods show a wide variation in the correlations between estimated and predicted compressive strength. Partially destructive methods are noted to provide correlations with good consistency between estimated and predicted compressive strength. -> Link to full text in repository
Stochastic input data brings aleatoric and epistemic uncertainty to the rack seismic analysis. From the synthetic acceleration-time history of the earthquake to the heterogeneous features of the rack system, several sources of uncertainty exist. The manufacturing process itself may produce slight deviations in the dynamic properties and mass distribution of the rack units. Moreover, each unit is loaded with a different number of fuel elements according to the operation needs of the plant. Even the exact clearance spaces between units are hardly inspectable due to radioactive ambiance. Hence, all of these uncertainties propagate across the nonlinear transient analysis and affect the accuracy and robustness of the numerical outputs. This paper carries out a ‘one-factor-ata-time’ parametric analysis of five key input variables: acceleration time-history, rack mass, fuel loading, rack Eigen-frequencies and hydrodynamic masses. This technique examines the impact on the main transient outputs when an analysis parameter is systematically varied while the others remain at their nominal value. Numerical results are provided for a simple two-rack system as a source of insight into the uncertain seismic response of a real rack system. It is highlighted that the dispersion is much higher for the sliding displacements than for the maximal forces on support.
Nuclear power plants are responsible for the spent fuel management. Closely spaced racks submerged in a pool are generally used to store and to cool the nuclear fuel. A free-standing design allows to isolate the rack base from the pool floor and therefore to reduce the impact of seismic loads. However, the seismic response of free-standing racks is difficult to predict accurately using theoretical models given the uncertainties associated with inertial forces, geometrical nonlinearities and fluid-structure interactions. An ad-hoc analysis methodology has been developed to overcome these difficulties in a cost-effective way, but some dispersion of results still remains. In order to validate the analysis methodology, experimental tests are carried out on a scaled 2-rack mock-up equipped with fake fuel assemblies. The two rack units are submerged in free-standing conditions inside a rigid pool tank and subjected to accelerations on a unidirectional shaking table. A hydraulic jack induces a given acceleration time-history while a set of sensors and gauges monitor the transient response of the system. Accelerometers track the acceleration of the pool and units. Load cells measure the impact forces on the rack supports as well as the fluid forces at the centre of the rack faces. Video cameras record the transient displacements and rotations. Results provide evidence of a water-coupling effect leading to an in-phase motion of the units. -> Link to full text in repository
The computation of the rack seismic response requires an implicit transient analysis with numerical integration of the differential equation of motion. It involves the solution of thousands of time steps throughout the whole earthquake duration. A series of Newton-Raphson trial iterations seek to establish equilibrium within a certain tolerance at each calculation step. The parameters related to such analysis are decisive in the computation of robust and accurate results. This paper carries out a ‘one-factor-at-a-time’ parametric analysis of six key analysis parameters for a simple two-rack system: maximal step size, maximal number of equilibrium iterations, convergence tolerance and Rayleigh and algorithmic damping. This technique examines the impact on the main transient outputs when an analysis parameter is systematically varied while the others remain at their nominal value. Numerical results provide a source of insight into the uncertain seismic response of the rack system and an effective tool to propose an efficient trade-off regarding the computational cost. -> Link to full text in repository

Spent fuel racks are steel structures designed to store the spent fuel assemblies removed from the nuclear power reactor. They rest in free-standing conditions submerged in the depths of the spent fuel pool. During a strong-motion earthquake, racks undergo large displacements subjected to inertial forces. An accurate estimation of their response is essential to achieve a safe pool layout and a reliable structural design. A transient analysis with direct integration of the equation of motion throughout the whole earthquake duration becomes therefore unavoidable. The computational cost associated to this analysis leads to the use of simplified finite element models giving rise to a certain dose of uncertainty. This paper carries out a parametric analysis of the key modelling properties for a two-rack system. This technique examines the behavior of the main transient outputs as a modelling parameter is systematically varied. Numerical results provide a source of insight into the general behavior of the rack system and an effective tool to propose an efficient and reliable modeling and meshing. The trade-off between outputs and computational cost and is also discussed. [DOI] -> Link to full text in repository

High Density Spent Fuel Storage (HDSFS) racks are structures designed to hold nuclear spent fuel assemblies removed from the nuclear power reactor after having been irradiated. They are used in the first step of the waste management process, during the wet storage. The underwater seismic response of HDSFS racks is a troubling safety issue. Since they are 12 m submerged free standing multi-body structures loaded with radioactive fuel, their design remains as complex as crucial. The design deals with a Fluid-Structure Interaction problem, a transient dynamic response and a very highly nonlinear behaviour. Several cost-effective industrial approaches have been used in these calculations to date, but some dispersion of results still exists. Therefore, the regulatory authorities are requiring an evaluation of the uncertainties in the methodology. Equipos Nucleares, S.A. (ENSA) is a worldwide expert in racks design and construction and has recently launched a research project to improve the understanding of the phenomena. The latter is funded by the European Commission and aimed to identify, evaluate and reduce the uncertainties involved in the calculations. In this paper, the state of the art and the current sources of uncertainty are discussed. -> Link to full text in repository
High Density Spent Fuel Storage racks are steel structures designed to hold nuclear spent fuel assemblies removed from the nuclear power reactor. Weighing around 60 tons, they are 5m high free standing structures resting on the floor of a 12 m depth pool and separated by only a few centimetres. Their underwater seismic response is a troubling safety issue, especially after Fukushima nuclear disaster. However, only limited basic guidelines have been provided as regulatory design criteria to date. The racks’ design deals with a very highly nonlinear behaviour, a transient dynamic response and a fluid-structure interaction problem. Industry is currently using available computer-aided finite element analysis software to solve the design problem in a cost-effective manner but some dispersion of results still exists. Hence, the nuclear regulatory authorities are requiring an evaluation of the current uncertainty associated to the assessment of rack displacements, rocking and maximum forces on supports. This paper discusses the main difficulties faced during the seismic analysis and presents an ad-hoc analysis methodology based on the hydrodynamic mass concept which takes advantage of a simplifying thermal analogy. The methodology, implemented in ANSYS FE Mechanical is hereby described for a reduced scale 2-rack model where the coupling effect of water in the dynamic motion of immersed racks is quantified and displacements and forces are provided. Finally, methodology assumptions are discussed and lessons learnt about the behaviour trends are summarized. -> Link to full text in repository
Analysis of Offshore Wind Turbine (OWT) fatigue damage is an intense, resource demanding task. While the current methodologies to design OWT to fatigue are quite limited in the way and amount of uncertainty they can account for, they still represent a relevant share of the total effort needed in the OWT design process. The robustness achieved in the design process is usually limited. To enable OWT to be more robust, an innovative methodology that tackles current limitations using a balanced amount of designing effort was developed. It consists of generating a short-term fatigue damage (DSH ) using a Kriging surrogate model that accurately accounts for uncertainty using an adaptive approach. The current paper discusses the application of a reinterpolation convergence to build a Kriging surrogate model that replicates DSH in OWT tower components. Different variables involved in the convergence are discussed. The discussion extends then to how the design could be improved by using different convergence scenarios for the Kriging surface. Cross-validation is used to train and validate the surrogate surface. The main goal is to give the designer a rationale on the trade-off between computational time and accuracy using the mentioned approach to design robust OWT towers. Results show that on a design basis two levels of approach may be efficient. In the first, if a very high computational cost is expected, a trade-off between accuracy and computational time must be considered and then, if the intention is to check how robust the current design is, a full convergence of the surface should be pursued.
The present work researches on the definition of the load spectra used for offshore wind turbine low SN slope materials’ fatigue design. Uncertainty in the sample sized used to scale fatigue life is analyzed for the tower component. Damage density is investigated for different environmental conditions in order to understand the importance of the different regimes of operation. Damage density is identified to be a heterogeneous function of the loading environmental conditions. In some cases, even for low SN slope materials, most of the damage occurs due to high load ranges. To study on the influence of this heterogeneity, different tail fits are used to compare the influence of accurately defining the tail region on a reference design time (𝑇). Results show that OWT fatigue is highly dependent on the 𝑡 shorter that 𝑇 time used to approximate 𝑇. This is mainly related to the fact that fatigue design depends not only on scaling stress ranges, but also cycle counts. Effort on the design phase should be applied in the definition of the uncertainty of the load spectra due to the limitation imposed by using low sample sizes to cover the extensive joint distribution of environmental parameters.
The current paper discusses the applicability of Gaussian process regressions, also known as Kriging models, in the context of structural and reliability analysis. Due to their flexibility these models appear in the field of structural analysis in many forms. Applications to approximate limit state functions, replace the computational expensive codes that solves the dynamic of complex systems, or replicate stochastic fields can be identified. Due to this fact, a discussion on the different parameters that depend on the implementation procedure chose to use these model is presented in the current paper. Design of experiments, polynomial approximation, correlation function, hyperparameters convergence and estimation function are the main global variables analysed. When implementing a Gaussian regression or Kriging model, the user is faced with the choice of these before any further progress. The discussion presented complements previous works on the implementation of such models in the sense that it focus on the structural analysis application and on how these parameters influence the accuracy. It is shown that depending on the approximation, significant advantage can be taken from understanding these major variables. Different examples are presented to support the understanding of the problem and the main conclusions on the applicability of the Gaussian regression models as surrogates for structural analysis are drawn.
For complex systems, the applicability of surrogate models has shown the potential to enable accurate assessments using a reduced batch of data and to compile information about large datasets. These behave as black-box functions that replace a series of inputs/outputs. In the present work, a Kriging surrogate is used to predict confidence intervals in an offshore wind turbine tower fatigue design. Uncertainty in fatigue due to loading is highly connected to the mean. One year operational fatigue results is used to validate the results. The Kriging is applied to replicate the yearly states of operation, and successfully predicts intervals of confidence for the long-term fatigue design. Regarding the interest of data analysis, the approach implemented is characterized by its flexibility and capability of approaching any problem that can be characterized by a single variable. Being therefore an interesting tool in decision schemes where large datasets are available or prediction of unknown outputs is required.

The fatigue design of Offshore Wind Turbines (OWT) is one of the most resource demanding tasks in the OWT design process. Techniques have been developed recently to simplify the amount of effort needed to design to structural fatigue. This is the example of the usage of Kriging surrogate models. These may be used in OWTs design not only, to reduce the computational effort needed to analyse an OWT, but also to allow their design to be robust. Due to the stress variability and its non-linear character, the short-term fatigue damage variability is high, and converging the stochastic field approached by the surrogate model in relation to the real observations is challenging. A thorough analysis of the different components that load an OWT and are more critical for the tower component fatigue life is required, and therefore, presented and discussed in the current paper. The tower, jointly with the foundation, are particular components of the OWT regarding the fatigue analysis process. Statistical assessments of the extrapolation of fatigue loads for the tower and the influence of the environmental parameters in the short-term damage are presented in this paper. This sets a support analysis for the creation of the Kriging response surfaces for fatigue analysis. NREL’s 5MW monopile turbine is used due to its state of the art character. Five environmental variables are considered in the analysis. A sensitivity analysis is conducted to identify which variables are most prominent in the quantification of the short-term damage uncertainty in the tower. The decoupling of the different external contributions for the fatigue life is a major contribution of the work presented. Preliminary guidelines are drawn for the creation of surrogate models to analyse fatigue of OWT towers and the most relevant conclusions are presented in an industry-oriented design outline regarding the most critical random variables that influence OWT short-term fatigue calculation. -> Link to full text in repository

The probabilistic analysis of Offshore Wind Turbines (OWT) is not a new practice. The standards for designing OWT (IEC 61400 class) emphasizes that assessing uncertainty is of major importance inside the design chain. Still, major challenges related to the uncertainty and the probabilistic assessment pose to the sector and its development. The analysis of operational loads is one them. The problem of analyzing extreme responses or cumulated damage in operation during the design phase is significantly related to its high computational cost. As we progressively add complexity to the system to account for its uncertainties, the computational effort increases and a perceptive design becomes a heavy task. If an optimization process is then sought, the designing effort grows even further. In the particular case of fatigue analysis, it is frequent to not be able to cover a full lifetime of simulations due to computational cost restrictions. The mentioned difficulties fomented the utilization of surrogate models in the reliability analysis of OWT. From these surrogate approximations the ones based on Kriging models gained a special emphasis recently for structural reliability. It was shown that, for several applications, these models can be efficient and accurate to approximate the response of the system or the limit state surfaces. The presented paper tackles some of the issues related to their applicability to OWT, in a case specific scenario of the tower component subjected to operational fatigue loads. A methodology to assess the reliability of the tower component to fatigue damage is presented. This methodology combines a Kriging model with the theory of extreme values. A one-dimensional Kriging case using the state of art NREL’s monopile turbine is presented. The reliability of the OWT tower is calculated for 20 years. The results show that the usage of a Kriging model to calculate the long term damage variation shows a high potential to assess the reliability of OWT towers to fatigue failure. -> Link to full text in repository

Offshore wind energy experienced an exponential growth in installed power since the beginning of the current century. While this growing trend is expected to continue, further growth of the sector imposes more demanding engineering methods. It is then envisaged that enhanced technical competitiveness can be achieved through a progressively less deterministic design process. Under the described context, a comparative study on the applicability of different probabilistic methods to estimate the probability of failure (Pf) of offshore wind turbine (OWT) towers under extreme events is presented here. Depending on the complexity introduced in the analysis of the OWT towers the applicability of different probabilistic approaches may be limited. FORM, SORM, Monte Carlo Simulation are examples of well-established methodologies to estimate Pf. Nevertheless, alternative methodologies such as the directional simulation can be an even more efficient solution for the problem. This preliminary assessment of the probabilistic approaches enables further developments in reliability methodologies for the specific case of OWT towers. -> Link to full text in repository
Marine and offshore engineering has long been challenged with the problem of structural integrity management (SIM) for assets such as ships and offshore platforms due to the harsh marine environments, where cyclic loading and corrosion are persistent threats to structural integrity. SIM for such assets is further complicated by the very large number of welded plates and joints, for which condition surveys by inspections and structural health monitoring become a difficult and expensive task. Structural integrity of such assets is also influenced by uncertainties associated with materials, loading characteristics, fatigue degradation model and inspection method, which have to be accounted for. Therefore, managing these uncertainties and optimizing the inspection and repair activities are relevant to improvements in SIM. This paper addresses probabilistic inspection planning and optimization by comparative analysis for a typical fatigue-prone structural detail based on reliability, life cycle cost (LCC) and value of inspection information (VoI). With the objective of clarifying the differences between the theoretical basis and objectives for probabilistic inspection optimization, three maintenance strategies for the structural detail are proposed and studied. It is found that different optimal inspection times are obtained with the objectives of reliability maximization, LCC minimization and VoI maximization. Also, planned inspection and repair can help to achieve higher reliability with fewer repairs than repair without inspection (i.e. time-based replacement). If the cost of unit inspection and repair is not negligible compared with failure consequence, it is suggested to employ the optimization objective of life cycle cost minimization, which considers the costs of SIM. The paper proposes a simple approach for quantifying the VoI, based on life cycle cost analysis for the three maintenance strategies. It is concluded that the VoI is relevant to both the optimal maintenance decision with and without inspection.  

There is a need to consider repair delay and incurred failure risk in maintenance optimization for some fatigue-critical structural details in marine and offshore structures. For example, in some cases, immediate repair may not be feasible due to weather, geographical location and/or technical restrictions. Also, immediate repair may be much more expensive than well-organized delayed repair. Moreover, detected cracks may sometimes be left unattended until more cracks are found and repaired together. This paper investigates a probabilistic maintenance optimization method allowing for repair delay and the incurred failure risk. The maintenance strategy considering repair delay is optimized based on uncertainty modeling, reliability and life-cycle cost analysis. Special features of the maintenance strategy and its impacts on fatigue reliability and life-cycle costs are discussed on an illustrative example. A method to quantify the risk incurred by repair delay is proposed. It is found that repair delay can result in a significant decrease in fatigue reliability if inspection is scheduled in the late stage of service life. The benefits of the maintenance strategy to fatigue reliability and life-cycle costs are very sensitive to the inspection method. The failure risk incurred by repair delay would be the predominant risk in the life cycle. -> Link to full text in repository

Maintenance scheduling and optimization against fatigue failures is of great interest for marine and offshore engineering in terms of safety assurance, integrity management and cost control. The main challenge is to make risk-informed and optimal maintenance decisions taking into account uncertainties associated with material properties, fatigue loads, modelling, inspection and maintenance methods. While optimization of inspection times has been the objectives of many studies, the influence and optimization of inspection qualities is not very clear. This paper has applied probabilistic fracture mechanics and reliability/risk methods to optimization of inspection quality as well as inspection time and revealed the effect of inspection quality on lifetime fatigue reliability. It is found that there is a reliability-based optimum inspection quality for maintenance scheduling, which is different from the cost-based optimum one. Better inspection quality than the optimum one can lead to excessive maintenance, which occurs when the effect of maintenance is not good, and the inspection quality applied is very good. Excessive maintenance can lead to increase of both expected failure costs and maintenance costs, and thus should be avoided. -> Link to full text in repository

Non-destructive testing (NDT) methods have been widely used for damage examination and structural maintenance, e.g. detecting and repairing fatigue cracks. In-service inspections help to increase fatigue reliability by providing new information for updating structural failure probability and making decisions on repair. However, these benefits are often compromised by uncertainties associated with inspection methods. Sometimes existing cracks may not be identified, and positive inspection indication may not exist. It is of great interest to consider the influence of inspection uncertainty in maintenance optimization because the benefits and costs of maintenance are affected by inspection decisions (inspection times and methods) which are subjected to inspection uncertainty. However, the influence of inspection uncertainty on maintenance optimization has not been explicitly and adequately covered in the literature. In this paper, the problem has been investigated by probabilistic modelling of the qualities of inspection methods via probability of detection (PoD) functions. A new PoD function has been proposed to characterize the inspection quality when inspection uncertainty is not considered. Optimum inspection decisions are derived with the objective of maximizing lifetime reliability index under two scenarios (considering and not considering inspection uncertainty). The effectiveness of a planned inspection is defined based on the max reliability indexes under the two scenarios. It is shown that the max lifetime reliability index generally deceases when inspection uncertainty is considered. However, inspection uncertainty may have little influence on the lifetime reliability index depending on the planned inspection time. The effectiveness of a planned inspection increases with the decrease of the mean detectable crack size. -> Link to full text in repository
 This paper addresses challenges in fatigue management of marine structural assets with a holistically approach, by jointly considering fatigue design, inspection and maintenance decisions, whilst taking into account sources of uncertainties affecting life cycle performance. A risk-informed and holistic approach is proposed for jointly optimizing fatigue design, inspection and maintenance based on the same fatigue deterioration model. The optimization parameters are fatigue design factor (FDF) and inspection intervals, while the objective is to minimize expected life cycle costs (LCC). The framework is to guide design process as well as to formulate optimal maintenance strategies. The proposed approach is exemplified for the marine industry through a fatigue-prone detail in a ship structure to obtain the life cycle optimal management solution that achieves a best compromise between structural safety and life cycle costs. -> Link to full text in repository

Fatigue cracking is a common problem that needs to be managed in the life cycles of steel structures. Operational inspections and repairs are important means of fatigue crack management. Driven by high relevance in safety control and budget saving, inspection and maintenance planning has been widely studied. However, the value of inspection and repairs has typically not been fully appreciated and quantified rationally before they are implemented. The basic idea of this paper is to address the planning problem with focus on repair other than on inspection. A maintenance strategy without inspection is studied and serves as comparison of a maintenance strategy with inspection. Then the value of repair and the value of inspection relative to repair can be evaluated respectively. An illustrative example is performed on a typical fatigue-prone detail in steel structures. -> Link to full text in repository

Fatigue cracks threaten integrity of marine and offshore assets and need to be managed properly during the life cycles. However, the decision making process for fatigue design and maintenance are often disconnected and probably not be optimal with respect to life cycle total costs. This paper proposes a holistic decision support tool for jointly optimizing fatigue design, inspection and maintenance decision based on risk quantification and life cycle cost analysis, taking into account the uncertainties associated with fatigue deterioration, inspection performance and repair effect. The tool can be used to support risk-informed fatigue design; inspection and maintenance decision making, so that fracture risk associated with design and operation of marine assets are controlled with the minimum life cycle total costs. ->Link to full text in repository

Efficient inspection and maintenance are important means to enhance fatigue reliability of engineering structures, but they can only be achieved efficiently with the aid of accurate pre-diction of fatigue crack initiation and growth until fracture. The influence of crack initiation on fatigue life has received a significant amount of attention in the literature, although its im-pact on the inspection plan is not generally addressed. Current practice in the prediction of fatigue life is the use of S-N models at the design stage and Fracture Mechanics (FM) models in service. On the one hand, S-N models are relatively easy to apply given that they directly relate fatigue stress amplitude to number of cycles of failure, however, they are difficult to extrapolate outside the test conditions employed to define the S-N curves. On the other hand, FM models like the Paris propagation law give measurable fatigue damage accumulation in terms of crack growth and have some ability to extrapolate results outside the test conditions, but they can only be a total fatigue life model if the initial crack size was known given that they do not address the crack initiation period. Furthermore, FM models generally introduce large uncertainties in parameters that are often difficult to measure such as initial crack size, crack growth rate, threshold value for stress intensity factor range, etc. This paper proposes a modified FM model that predicts the time to failure allowing for crack initiation period. The main novelty of the modified FM model is the calibration using S-N data (i.e., inclusive of crack initiation period) for an established criterion in fatigue life and reliability level. Sources of uncertainty associated to the model are quantified in probabilistic terms. The modified FM model can then be applied to reliability-based inspection planning. An illustrative example is performed on a typical detail of ship structure, where the optimum inspection plan derived from the proposed model is compared to recommendations by existing FM models. Results demonstrate to what extent is the optimum inspection plan influenced by the crack initiation period. The modified model is shown to be a reliable tool for both fatigue design and fatigue management of inspection and maintenance intervals. -> Link to full text in repository

A problem with fracture mechanics (FM) based fatigue analysis is that reliable information on initial crack/flaw size is often hard to obtain. Also, FM method can’t be applied directly to welded joints with relatively small initial flaws and long crack initiation life. This paper proposes a novel probabilistic FM method based on the equivalent initial flaw size (EIFS) concept. The initial crack size is substituted with EIFS to take both the crack initiation and propagation life into account. Three methods are tested to obtain mean value of EIFS: calibrating to S-N curves, Kitagawa-Takahashi (KT) diagram and fitting to test data. The obtained EIFSs are evaluated by comparing the predicted fatigue lives and crack evolutions with S-N curves and test crack evolution data. The suggested procedure is to derive the mean value of EIFS from S-N curves and the coefficient of variation from KT diagram. -> Link to full text in repository

Fatigue cracks pose threats to the integrity of welded structures and thus need to be addressed in the whole service lives of structures. In-service inspections are important means to decease the probability of failure due to uncertainties that cannot be accounted for in the design stage. To help schedule inspection actions, the decline curve of reliability index with time needs to be known. A predictive tool is normally developed based on crack propagation models neglecting the crack initiation stage, which leads to conservative predictions for fatigue life. Inspection plans built on those predictions are far from optimal, especially for welds with relatively long crack initiation life. This paper proposes to use a fracture mechanics based reliability analysis method that takes the crack initiation stage into account via the concept of Time-To-Crack-Initiation (TTCI). The optimum inspection plan for a fatigue prone ship structural component is derived by the new approach and compared to the commonly-used method that only considers crack propagation life. Two inspection planning approaches are tested to investigate the influence of incorporating crack initiation period: (i) target reliability approach and, (ii) equidistant inspection times approach. With each planning approach, two inspection methods are adopted: close visual and magnetic particle inspection. The paper concludes with recommendations on the inspection method and planning approach to adopt while considering and without considering the crack initiation stage. [DOI] -> Link to full text in repository

Crack initiation and propagation threatens structural integrity of welded joints and normally inspections are assigned based on crack propagation models. However, the approach based on crack propagation models may not be applicable for some high-quality welded joints, because the initial flaws in them may be so small that it may take long time for the flaws to develop into a detectable size. This raises a concern regarding the inspection planning of high-quality welded joins, as there is no generally acceptable approach for modeling the whole fatigue process that includes the crack initiation period. In order to address the issue, this paper reviews treatment methods for crack initiation period and initial crack size in crack propagation models applied to inspection planning. Generally, there are four approaches, by: 1) Neglecting the crack initiation period and fitting a probabilistic distribution for initial crack size based on statistical data; 2) Extrapolating the crack propagation stage to a very small fictitious initial crack size, so that the whole fatigue process can be modeled by crack propagation models; 3) Assuming a fixed detectable initial crack size and fitting a probabilistic distribution for crack initiation time based on specimen tests; and, 4) Modeling the crack initiation and propagation stage separately using small crack growth theories and Paris law or similar models. The conclusion is that in view of trade-off between accuracy and computation efforts, calibration of a small fictitious initial crack size to S-N curves is the most efficient approach. -> Link to full text in repository
In the current paper, the impact of the hoisting operations, on the dynamic response of the lifting boom of a ship unloader, are taken into consideration. The lifting boom is used to carry out transient dynamic analysis, since it was recognized to be the single most representative element for studying the dynamic response of these structures. The response of the lifting boom was the result of dynamic analysis comprising two components: the structure (representing the waterside portion of the boom), and the applied load (expressed as different hoisting force profiles). A comparison between the different force profiles was carried out, in order to identify the parameters that mostly influence the dynamic behavior of the structure during a loading cycle. Furthermore, a baseline case based on pseudo-static analysis (as standard recommendation FEM 1987) was introduced and comparisons carried out in terms of vertical displacement and bending moments.
Here, the TRUSS (Training in Reducing Uncertainty in Structural Safety) ITN (Innovative Training Network) Horizon 2020 project (http://trussitn.eu, 2015-19) demonstrates how accuracy of residual life assessment predictions can be improved by achieving a good agreement between measured and predicted dynamic responses of a crane structure. Existing records of measured strain data are often missing information such as the weight of the payload, the hoisting speed and acceleration that are relevant for structural assessment purposes. This paper aims to reduce uncertainties associated to the recorded data in an aged grab ship unloader by comparing measured and non-linear transient finite element analyses results for a loading/unloading cycle. The speed pattern is determined from a best match to the measured record. The moving load consisting of ‘trolley + grab + payload’ is modelled with parameters that are derived from minimizing differences between measured and simulated responses. The determination of these loading parameters is central to accurately assess the remaining life of ship unloaders. -> Link to full text in academic repository
Container cranes represent an important link in the maritime transport system. Assessment of residual life for such cranes is important both in terms of safety and cost of repair and maintenance. These cranes usually have a hoisting trolley system which can move along the boom for lifting, carrying and lowering the payload, loading/unloading vessels in the harbour. This paper investigates the dynamic response of the lifting boom using a non-linear finite element analysis. A number of such moving trolley systems, with different degrees of complexity, are modelled to assess the impact of their influence on the boom dynamic response parameters. Results from the finite element analysis are compared to a pseudo-static analysis and are presented in terms of a Dynamic Response Factor (DRF).-> Link to full text in repository
This paper highlights the impact of dynamic amplification factors in remaining fatigue life assessment of ship unloaders. In practice, the widely accepted procedure for these structures is to carry out a fatigue life assessment envisages: (1) carrying out static analysis, (2) taking into account dynamics via the application of dynamic amplification factors, and (3) applying Miner’s rule. This factor, provided by the standard, is applied to the structure as a whole without considering the vibration of each structural member individually. This paper characterizes the dynamic behavior of each element using location-based dynamic amplification factors estimated from measurements. This caters for a more accurate assessment of the structure, whilst maintaining the simplicity of the standard procedure. [DOI] -> Link to full text in repository
This paper reviews methodologies for fatigue analysis with emphasis on ship unloaders. Maintaining the performance of ship unloaders at a satisfactory level is essential for any port’s operation in order to comply with the global demand of shipping and trading. Ship unloaders are subject to alternating operational loadings and to adverse environmental conditions, and as a result, they show a rapid rate of deterioration that makes them susceptible to failure by cumulative damage processes such as corrosion and fatigue. The purpose of this paper is to review key features of the most common methodologies for fatigue analysis and to underline the limitations and uncertainties involved. Finally, developments in reliability-based approaches are suggested for a more accurate fatigue assessment of ship unloaders. -> Link to full text in repository
This paper reviews the most common causes of failure in ship unloaders. The structural forms employed in the design of ship unloaders and the characteristics of the loads acting on these structures are introduced first. Then, typical failures including overloading, joint failure, cable breaking, corrosion and fatigue failure amongst others, are described. Fatigue failure is discussed in further detail. When assessing a ship unloader for fatigue, it is necessary to define the fatigue demand and the fatigue strength capacity of those structural details under investigation. The latter experiences stress cycles that accumulate over time until reaching a limit that leads to cracking. Loads and stresses need to be monitored to describe those cycles, and critical locations must be checked to prevent a catastrophic failure. -> Link to full text in repository

About WP5. Rail and Road Infrastructure

This paper presents a case study that reveals useful information to a stakeholder in verifying the condition of its bridge structure. It is demonstrated through a field testing carried out on an abutment of a historical railway bridge that exhibits a vertical crack on its wing wall running through its full height. Although this does not pose danger to normal traffic it was observed that the crack moves under live load and obtaining real behaviour of the crack movement was crucial for the stakeholder to assess the condition of the structure, which is the main focus of this study. This is tackled by monitoring the crack movement on the abutment wing wall using an optical camera system pointing to optical targets attached on the structure. As a result of this study, three-dimensional crack movement under train loading is obtained and the response of the test structure to a train loading is further analysed within the scope if this study.
‘This paper proposes a new axle detection methodology using direct strain measurements. Initially, numerical analyses are carried out on a 1-D simply supported bridge structure to investigate the strain response of a bridge to a 2-axle moving vehicle. The strain response obtained from the numerical model is further studied and a new axle detection strategy, based on the second derivative of strain with respect to time, is proposed. Having developed the theoretical concept, field testing is carried out on a single span simply supported railway bridge to validate the proposed methodology and test its robustness of on a full-scale bridge.
Structural health monitoring has proven to be a useful tool for evaluating the condition of bridges, with permanent monitoring systems installed on long span bridges forming vital links on the major transport routes. Economic demands often reduce the availability of monitoring systems for the smaller bridges which make up the wider transport network. A short-term monitoring system is designed to be easily and quickly installed and can be adjusted to suit the individual requirements of bridges. These systems are ideal for rural regions where there are a high number of bridges on isolated road and rail networks.

This report will review a study of a single span bridge on a private heritage railway in England under varying loading conditions. The loading conditions were supplied by the passing steam engines, including the Flying Scotsman. The study was designed to measure static and dynamic measurements of the bridge under loading from passing steam trains. Accelerometers were used to determine the rotations and deflections of the bridge deck under loading from the trains. To verify the results, measurements of deflection at mid-span were taken using a video based measurement system. The results showed that the proposed method provides high accuracy when compared to the video imagery measurements.

Monitoring displacement of in operation bridges is practically challenging but potentially very useful for condition assessment and decision support. The primary difficulties are in finding fixed physical reference points and, for the majority short span bridges under normal operation, the mm-level magnitudes of displacement under normal operating conditions (e.g. standard truck loading). With rare possibility for physical connection between a reference and a bridge, non-contacting technologies such as GPS need to be used. Other options include total station and more exotic technologies of laser interferometer and radar have also been tried. There are drawbacks for each technology related to limited sample rate (for total station) and signal to noise ratio (for GPS) while radar and laser are expensive and require specialist users. With advances in computing power, optics-based systems are becoming popular, relying on a standard lens but with capability to track multiple positions with potential to recover deformation with high spatial resolution. This paper reports the experiences of the authors exploring the suitability of a commercially available optics-based system in terms of spatial and temporal resolution and sampling and in challenging field conditions required for long term monitoring. For example issues such as stability of camera mounting (e.g. in wind) and varying lighting conditions while not problematic in a laboratory govern performance in the field. The paper tracks a sequence of experiments moving from lab to field, ultimately moving up to a field test on a road bridge in Devon. In each case the capabilities and limitations of the system have been critically examined. The study has defined both limitations and capabilities, while defining best approaches for use and at the same time providing some useful performance data on the subject bridges. 
This paper showcases the importance of field testing in efforts to deal with the deteriorating infrastructure. It demonstrates a load test performed on a healthy but aging composite reinforced concrete bridges in Exeter, UK. The bridge girders were instrumented with strain transducers and static strains were recorded while a four-axle, 32 tonne lorry remained stationary in a single lane. The results obtained from the field test were used to calculate transverse load distribution factors (DFs) of the deck structure for each loading case. Additionally, a 3-D finite element model of the bridge was developed and calibrated based on field test data. Similar loading cases were simulated on the analytical model and behaviour of the structure under static loading was studied. It was concluded that the bridge support conditions had changed throughout its service life, which affected the superstructure load distribution characteristics. Finally, DFs obtained from analysis were compared with factors provided in Design Manual for Roads and Bridges Standard Specification for similar type of bridges. -> Link to full text in repository
Probabilistic assessment of ageing structures has become an important research area as it attracts the interest from not only researchers but also investors, municipalities and governments. The most commonly used material for many important structures and infrastructure is reinforced concrete. Various degradations of such structures are manifest in the form of direct loss of reinforcement area. In this study a time dependent stochastic model of the reinforcement loss (in [%]) due to corrosion is presented, which has a crucial role in the estimation of the lifetime and the time-dependent health state of the structure. Bayesian updating is applied in multiple steps during the lifetime of the structure in order to improve the estimate of the reinforcement loss. An example application is shown where updating is applied in two steps.
The influence line of a structure reflects its structural behaviour as well as any possible damage present on the bridge. An iterative algorithm is presented in this paper in order to obtain the shape of the influence line of a bridge together with the load distribution of trucks passing overhead. One great advantage of this approach is that sensor calibration with pre-weighed trucks can be avoided. The only initial information needed are the measurement data and a preliminary estimate of influence line based on engineering judgement. An illustrative example is shown, where strain data have been collected on a reinforced concrete culvert. Apart from the efficiency of the proposed algorithm, the influence of the temperature on the results is also shown.
Probabilistic assessment of ageing structures has become an important research area as it interests not only researchers but investors, municipalities and even governments. The most commonly used material for structures and physical infrastructure is reinforced concrete. Important degradations of such structures are caused by corrosion of the reinforcement either directly (reinforcement loss) or indirectly (extensive cracking). In this study a common way of corrosion modelling of reinforced concrete structures is presented. By considering the relevant uncertainties it is possible to develop a time-dependent stochastic model of the reinforcement loss (in [%]) due to corrosion, which is the focus of this study. This parameter has a key role in the estimation of the lifetime and the time-dependent health state of the structure. The related uncertainties, however, increase significantly with time. Therefore Bayesian updating is applied multiple times during the lifetime of the structure in order to improve the prediction of the reinforcement loss due to corrosion. The effect of this updating, assuming different possible ways of data collection, are shown and the advantages of such updating are briefly discussed.

Probabilistic assessment of ageing bridges has become an important research area as it interests not only researchers but investors, municipalities and even governments. In this paper a simple bridge model is presented in a probabilistic context. A comparative study is carried out involving damage indicators and Bayesian updating. Bayesian updating is a powerful tool, which has been used in various research areas. However, using it for approximating the safety level of a bridge is challenging due to the various sources of uncertainties that may affect the performance of a measurement based damage indicator. The effects of different factors involved in the updating are examined in this paper and compared. [DOI] -> Link to full text in repository

This paper introduces the various aspects of bridge safety models. It combines the different models of load and resistance involving both deterministic and stochastic variables. The actual safety, i.e. the probability of failure, is calculated using Monte Carlo simulation and accounting for localized damage of the bridge. A possible damage indicator is presented in the paper, which is based on measured deflection data. With the use of this chosen damage indicator, Bayesian updating is performed on the bridge safety model. In the last part an example application is presented including examining the sensitivity of the updating, regarding the different levels of confidence in the assumed prior distribution.
This paper introduces the various aspects of bridge safety models. It combines the different models of load and resistance involving both deterministic and stochastic variables. The actual safety, i.e. the probability of failure, is calculated using Monte Carlo simulation and accounting for localized damage of the bridge. A possible damage indicator is also presented in the paper and the usefulness of updating the developed bridge safety model, with regards to the damage indicator, is examined.
Probabilistic assessment of bridges has been the subject of various studies in recent decades. It has been widely agreed that evaluating an existing bridge according to the standards and codes used for new structures can lead to demolition of a safe bridge or unnecessary repairs, and thus to high economic cost and an increase in the associated environmental impact. This paper investigates several concerns, the sensitivities of and correlation between the different stochastic parameters influencing the load on a bridge and its resistance to that load. The usefulness of updating the bridge safety model using damage indicators from a Structural Health Monitoring system is also examined.

The proposed approach combines a number of aspects. Firstly, a probabilistic bridge load model is established based on Weigh-In-Motion (WIM) data to mimic a realistic traffic flow and hence, the loads and their effects on the bridge. Traffic loading is highly correlated as the same vehicles influence many parts of the bridge. This has a significant influence on the probability of failure.

To model the resistance of the bridge a probabilistic approach is used and full correlation between segments is assumed. Combining the load and resistance models, the probability of failure can be inferred. In the future work the bridge safety model, more precisely the resistance model, will be updated. Bayesian updating will be used in the current framework based on the information obtained from specific damage indicators.

This study aims at obtaining valuable information regarding the importance of the different aspects of bridge safety models and the sensitivity of the probability of failure (i.e. the level of safety) to them. It is also expected to confirm the applicability of a Bayesian approach to this problem. -> Link to full text in repository

Between February 2016 and April 2017 seven bridges around the world collapsed suddenly, by causing fatali-ties and significant economic losses. Structural Health Monitoring (SHM) techniques can assess the health state of an infrastructure by analysing its behaviour, and point out degraded elements that require to be main-tained. As a consequence, SHM methods can improve the safety of the transportation network, and reduce the life cycle costs of the infrastructure, by optimizing the maintenance schedule. In this paper, a Bayesian Belief Network (BBN) approach is proposed to assess the health state of a beam-and-slab bridge, by relying on the analysis of its vertical acceleration. A procedure to robustly define the Conditional Probabilities Tables (CPTs) of the BBN is proposed, by merging expert judgement with information about the bridge behaviour. The BBN allows to update the health state of the bridge when a new measurement of the bridge acceleration is available, by taking account of the health state of each element of the bridge. In this way, the degradation level of the whole bridge and of its elements is monitored over time, and unexpected behaviour of the bridge are diagnosed.
Structural Health Monitoring (SHM) techniques are able to monitor the behaviour of critical infrastructure over time, by improving the safety and reliability of the asset. A large amount of data is generated by SHM methods continuously. Therefore, machine learning methods can be developed in order to transform the available data into valuable information for decision-makers, by pointing out vulnerabilities of the critical infrastructure. In this paper, a machine learning classifier for condition monitoring and damage detection of bridges is proposed by adopting a Neuro-Fuzzy algorithm. The method allows to assess the health state of the infrastructure automatically, accurately and rapidly, every time when a new measurement of the bridge behaviour is available. The method is validated and tested by monitoring the behaviour of an in-field steel truss bridge, which is subjected to a progressive damage process.
The UK railway network is subjected to an electrification process that aims to electrify most of the network by 2020. This upgrade will improve the capacity, reliability and efficiency of the transportation system by providing cleaner, quicker and more comfortable trains. During this process, railway infrastructures, such as tunnels, require to be adapted in order to provide the necessary clearance for the overhead line equipment, and consequently, a rigorous real-time health monitoring programme is needed to assure safety of workforce. Large amounts of data are generated by the real-time monitoring system, and automated data mining tools are then required to process this data accurately and quickly. Particularly, if an unexpected behaviour of the tunnel is identified, decision makers need to know: i) activities at the worksite at the time of movement occurring; ii) the predicted behaviour of the tunnel in the next few hours.

In this paper, we propose a data mining method which is able to automatically analyse the database of the real-time recorded displacements of the tunnel by detecting the unexpected tunnel behaviour. The proposed tool, first of all, relies on a step of data pre-processing, which is used to remove the measurement noise, followed by a feature definition and selection process, which aims to identify the unexpected critical behaviours of the tunnel. The most critical behaviours are then analysed by developing a change-point detection method, which detects precisely when the tunnel started to deviate from the predicted safe behaviour. Finally, an Artificial Neural Network (ANN) method is used to predict the future displacements of the tunnel by providing fast information to decision makers that can optimize the working schedule accordingly. -> Link to full text in repository

More than 35% of the European railway bridges are over 100 years old and the increasing traffic loads are pushing the railway infrastructure to its limits. Bridge condition-monitoring strategies can help the railway industry to improve safety, availability and reliability of the network. In this paper, a Bayesian Belief Network method for condition monitoring and fault detection of a truss steel railway bridge is proposed by relying on a fuzzy analytical hierarchy process of expert knowledge. The BBN method is proposed for obtaining the bridge health state and identifying the most degraded bridge elements. A Finite Element model is developed for simulating the bridge behaviour and studying a degradation mechanism. The proposed approach originally captures the interactions existing between the health state of different bridge elements and, furthermore, when the evidence about the displacement is introduced in the BBN, the health state of the bridge is updated. -> Link to full text in repository

More than 350,000 railway bridges are present on the European railway network, making them a key infrastructure of the whole railway network. Railway bridges are continuously exposed to changing environmental threats, such as wind, floods and traffic load, which can affect safety and reliability of the bridge. Furthermore, a problem on a bridge can affect the whole railway network by increasing the vulnerability of the geographic area, served by the railway network. In this paper a Bayesian Belief Network (BBN) method is presented in order to move from visual inspection towards a real-time Structural Health Monitoring (SHM) of the bridge. It is proposed that the health state of a steel truss bridge is continuously monitored by taking account of the health state of each bridge element. In this way, levels of bridge deterioration can be identified before they become critical, the risk of direct and indirect economic losses can be reduced by defining optimal bridge maintenance works, and the reliability of the bridge can be improved by identifying possible hidden vulnerabilities among different bridge elements. -> Link to full text in repository

Bridges are one of the most critical structures of the railway system. External loads may affect the bridge health state, and consequently their safety, availability and reliability can be improved by monitoring their condition and planning maintenance accordingly. In this paper, a Bayesian Belief Network (BBN) fault detection methodology for a truss steel railway bridge is proposed. The BBN is developed to assess the health state of the whole bridge using evidence about the behaviour of the bridge. In this initial study, the evidence is provided in terms of the values of displacement computed by a Finite Element model. -> Link to full text in repository

The majority of bridge condition assessment methods from acceleration data incorporate the use of the Fourier Transform (FT) to obtain or assess damage sensitive features, however the accuracy of the FT’s output for non-linear and non-stationary signals can causes a problem for real-world structural applications. The Hilbert–Huang Transform (HHT) has long been cited as a potential alternative to the FT for non-linear, non-stationary signals and has gathered popularity in the condition assessment of rotating machinery due to its time-frequency-energy representation. On the other hand, instances of the HHT being applied to bridge structures has been less common, predominantly due to the inconsistency of the required Empirical Mode Decomposition (EMD) phase of the methodology. The present paper utilises recent advancements in EMD methodology through the application of Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), which considerably reduces the undesirable decomposition effects of signal noise contamination. A novel damage parameter is proposed that utilizes the HHT’s instantaneous outputs to successfully achieve damage identification in a real bridge structure subjected to a progress damage test under single vehicle excitation.
Many methods of damage identification in bridge structures have focused on the use of either numerical models, modal parameters or non-destructive damage tests as a means of condition assessment. These techniques can often be very effective but can also suffer from specific pitfalls such as, numerical model calibration issues for nonlinear and inelastic behaviour, modal parameter sensitivity to environmental and operational conditions and bridge usage restrictions for non-destructive testing. The present paper covers alternative approaches to damage identification of bridge structures using empirical parameters applied to measured vibration response data obtained from two field experiments of progressively damaged bridges subjected to ambient and vehicle induced excitation, respectively. Numerous non-modal vibration-based parameters are detailed and selected for the assessment of either the ambient or vehicle induced excitation data based on their inherent properties.
Many traditional methods of damage identification in bridge structures implement numerical models and/or modal parameters as a means of condition assessment. While such techniques can often be effective, they may also succumb to their own intrinsic constraints, such as shortcoming in numerical model calibration to dynamic behaviour and environmental sensitivity of modal parameters. Furthermore, the degree of vibration signal non-stationarity that may be induced due to vehicle excitation can limit the applicability of some common signal processing techniques, such as Fourier transforms. The current study investigates vibration-based approaches to damage identification that circumvent some of these issues. Vibration data obtained from a real bridge structure subjected to a progressive damage test under vehicle induced excitation is used as a test subject. Novel vibration parameters obtained from the raw signals are assessed for their damage detection, localisation and quantification capabilities. Additionally, advanced Empirical Mode Decomposition (EMD) and the Hilbert-Huang Transformation (HHT) is applied to the non-stationary signals for the purpose of damage identification. The investigation shows that damage detection, localisation and quantification is achievable from the vehicle induced vibration signals using the proposed empirical techniques.
The process of damage identification in bridge structures traditionally involves the extraction of one or more of the numerous modal-based Damage Sensitive Features (DSFs) from vibration data obtained by direct sensor measurement. However the performance of these modal-based DSFs can suffer under changing environmental and operational conditions and are generally limited by their methodology, which assumes linear structural behaviour and signal stationary. The present paper presents a detailed overview of the development of alternative DSFs derived from vibration characteristics, focusing on their conception, damage sensitivity evaluation and performance robustness assessment. Initially, selected vibration parameters are outlined to the reader before their damage sensivity is determined on progressive damage test data obtained from a post-tensioned three-span bridge under ambient conditions using supervised machine learning techniques in conjunction with the Minimum Covariance Determinate (MCD) estimator to mitigate uncertainty surrounding sources of excitation. Secondly, the performance robustness of the vibration-based DSFs is assessed on highly non-stationary data obtained from a progressively damaged steel truss bridge subjected to vehicle excitation. Finally, a comparative evaluation of the vibration-based DSFs is made against modal-based DSFs performance from the literature. -> Link to full text in repository

Over the years, there have been numerous efforts by researchers in quantifying structural degradation and damage from vibration measurements. Traditionally, damage detection techniques in bridges have focused on the use of modal-based damage indicators, such as frequencies, mode shapes and mode shape derivatives. However, these parameters have been shown to be sensitive to environmental and operational variations and can be difficult to accurately extract under low-level ambient excitation. Recent research has found a correlation between certain vibration parameters, such as vibration intensity, and a group of damage bridges, suggesting that vibration parameters may detect damage if extracted correctly. The present study furthers these findings by examining a number of vibration parameters as damage indicators to discern their sensitivity to various condition states of a progressively damaged bridge under ambient excitation.[DOI] -> Link to full text in repository

The assessment of bridge condition from vibration measurements has generally been determined via the monitoring of modal parameters determined through adaptations of the standard Fast Fourier Transform (FFT) or other stationary time-series based transformations. However, the non-stationary nature of measured vibration signals from damaged structures can limit the quality of frequency content information estimated by such methods. The Hilbert–Huang Transform’s (HHT) ability to decompose non-stationary measured vibration data into a time-frequency-energy representation allows signal variations to be identified sooner than other stationary-based transformations, thus potentially allowing early detection of damage. The present study uses data obtained from a progressive damage test conducted on a real bridge subjected to excitation from a double axle passing vehicle as a test subject. Decomposed vibration signals from the HHT and associated marginal spectrums are assessed to determine structural condition for various damage states and different locations along the bridge. [DOI] -> Link to full text in repository

Overtime, the structural condition of bridges tends to decline due to a number of degradation processes, such as; creep, corrosion and cyclic loading, among others. Considerable research has been conducted over the years to assess and monitor the rate of such degradation with the aim of reducing structural uncertainty. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters and subsequently comparing them to numerical models. These traditional techniques are generally time-consuming and can often mistake changing environmental and operational conditions as structural damage. Recent research has seen the emergence of more advanced computational techniques that not only allow the assessment of noisier and more complex data, but also allow research to veer away from monitoring changes in modal parameters alone. This paper presents a review of the current state-of-the-art developments in vibration-based damage detection in small to medium span bridges with particular focus on the utilization of advanced computational methods that avoid traditional damage detection pitfalls. A case study of the S101 Bridge is also presented to test the damage sensitivity a chosen methodology. Finally, in the evaluation of the shear crack pattern, not only crack initiation and location are of importance, but also crack width, shear crack angles and shear sliding displacements along the cracks have to be measured to evaluate the shear performance of a structural element. -> External link to full text -> Link to full text in repository
Overtime, the structural condition of bridges tends to decline due to a number of degradation processes, such as; creep, corrosion and cyclic loading, among others. Considerable research has been conducted over the years to assess and monitor the rate of such degradation with the aim of reducing structural uncertainty. Traditionally, damage detection techniques in bridges have focused on monitoring changes to modal parameters and subsequently comparing them to numerical models. These traditional techniques are generally time-consuming and can often mistake changing environmental and operational conditions as structural damage. Recent research has seen the emergence of more advanced computational techniques that not only allow the assessment of noisier and more complex data, but also allow research to veer away from monitoring changes in modal parameters alone. This paper presents a review of the current state-of-the-art developments in vibration based damage detection in small to medium span bridges with particular focus on the utilisation of advanced computational methods, such as machine learning, pattern recognition and advanced data normalisation algorithms. -> Link to full text in repository
Over the years, there have been numerous efforts by researchers in quantifying structural performance and damage from vibration measurements. Curves proposed by several authors (Koch 1953, Steffens 1974) attempt to relate acceleration spectrums to damage level, which were determined based on experimental surveys conducted on buildings. The technique is focused on the use of vibration intensity, which is a function of acceleration amplitude and frequency, as a parameter to discern damage. Recently, some Codes have adopted vibration intensity criteria for evaluating damage, such as the Brazilian Code for non-destructive testing ABNT-NBR-15307 (2005), which reproduces Koch’s criteria for any kind of structure, including bridges. It states that vibration intensity is an empiric parameter used to estimate damage levels in structures, and can be ex-pressed in units known as vibrars. According to the Brazilian Code, there exists an empirical relationship between the values of vibrars and the level of structural damage: 10-30 (None), 30-40 (Small), 40-50 (Severe) and 50-60 (Failure) (ABNT,2005).

The present work investigates the use of vibrars and maximum peak-to-peak accelerations as parameters of damage and performance evaluation in existing bridges and also as a way to predict long-term performance during the initial design stage. To achieve this, a database of the most common Brazilian bridge types was analyzed, whose structural design and dynamic parameters are known. Measured traffic data and material properties were integrated into calibrated FEA models and a fatigue assessment was conducted.

A damage index compiled by Kim et al. (2005) was used to assess damage based on dynamic property variation and the general structural condition of the bridges, observed during detailed inspections. Measured vibration was subsequently assessed against the damage index and an additional reliability index to assess the bridges’ fatigue safety. This resulted in a clear correlation between maximum peak-to-peak accelerations and the indices; however, vibration intensity, measured in vibrars as suggested by ABNT-NBR-15307 (2005), did not produce good correlation with the indices. Not only worse correlation was observed in the case of vibrars, but also a tendency of damage decreasing with increasing vibrars, which is not reasonable. As a final result, from the observed correlations, limits of maximum peak-to-peak acceleration are proposed to be considered in existing and newly designed bridges to certify an acceptable long-term condition and safety against fatigue effects. -> Link to full text in repository

References.-
Associacao Brasileira de Normas Tecnicas – ABNT 2005. Ensaios não destrutivos – Provas de cargas dinâmicas em grandes estruturas – Procedimento. NBR 15307.
Kim, T.H., Lee, K.M., Chung, Y.S. & Shin, H.M. 2005. Seismic damage assessment of reinforced concrete bridge columns. Engineering Structures. Vol.27, No.11, pp 576-592.
Koch, H.W. 1953. Determining the effects of vibration in buildings, V.D.I.Z., Vol. 25, N. 21, pp. 744-747.
Steffens, R.J. 1974. Structural vibration and damage. – Building Research Establishment. London.

Distributed optical fiber sensors (DOFS) is one of the most promising and exciting technologies under research to be applied in the structural health monitoring (SHM) of civil engineering infrastructures. Therefore, in this paper, the authors present a laboratory experiment where a reinforced concrete beam was instrumented with a 5-meter-long polyimide DOFS in a way that four equal segments were bonded to the bottom surface of the beam using for each segment a different type of adhesive. Three strain gauges were also used to compare the results. The beam was then loaded, generating expected equal levels of strain for each of the segments allowing for a direct comparison between them. In this exercise, additionally to the comparison with the other instrumented sensors it is also important the consideration and analysis of the associated Spectral Shift Quality (SSQ) values of the DOFS measurements.
The use of fiber optic sensors on civil engineering structural health monitoring (SHM) applications have become quite popular for the past two decades. Within this type of sensors however, the study and use of Optical Backscatter Reflectometry (OBR) based Distributed Optical Fiber Sensors (DOFS) is relatively new. In this way, there is still some uncertainty that would allow the use of this technology in a more systematic and standardized way. Some of this uncertainty is related with the long-term reliability behavior of these sensors when applied on the monitoring of a structure under a large number of load cycles. In this way, the authors conducted a laboratory experiment where a reinforced concrete beam was instrumented with a DOFS that was adhered in a way to allow the measuring of strain on four different longitudinal segments on its bottom surface. A fatigue test was then conducted on this element where the inputted load range was the one expected on a standard highway bridge between its self-weight and the additional traffic load. Furthermore, each longitudinal segment of the DOFS was adhered to the concrete using a different adhesive in order to assess the optimal one in these conditions. The obtained data is then compared with strain gauges that are also instrumented on the concrete beam.-> Link to full text in repository
The authors conducted a laboratory experiment where two reinforced concrete beams were instrumented with a Distributed Optical Fiber Sensors that were adhered to allow the measuring of strain on four different longitudinal segments on their bottom surface. A fatigue test was then conducted on these elements where the load range was the one expected on a standard highway bridge between its permanent and the additional traffic load. Furthermore, each longitudinal segment of the DOFS was adhered to the concrete using a different adhesive in order to assess the optimal one in these fatigue conditions. The obtained data is then compared with strain gauges that were also instrumented on the concrete beams. -> Link to full text in repository
In this paper, the authors conducted an experiment where a reinforced concrete beam was instrumented with a single DOFS performing four equal segments externally bonded to the bottom surface of the element. Each segment was adhered to the concrete using a different adhesive. This beam was then loaded, producing expected equal levels of tension in each of the fiber segments for a more direct comparison of the different adhesives performance. The experimental data is subsequently compared with the data provided through more conventional electrical strain gauges that were also deployed in the beam. The effect of alternating the spatial resolution and sampling acquisition is also analyzed. The results provided here will shed a clearer light on how to proceed when applying this promising sensing technique to concrete structures, mainly on what the bonding materials and resolution are concerned. -> Link to full text in repository

In this paper, an experiment where distributed optical fiber sensors (DOFS) were implemented in two small concrete beams subjected to a three-point load test is outlined. Here, an optical backscatter reflectometry based DOFS is implemented simultaneously embedded in the concrete (glued to the steel rebar) and attached to the outer surface of the concrete after its hardening. For comparison purposes, three electrical strain gauges are also used in the rebar. The main objectives with this experiment, is to analyze the feasibility of installation of DOFS directly on the rebar element of a reinforced concrete beam and compare the measured strain at rebar and surface of the concrete. -> Link to full text in repository

In this work, an experiment on two small concrete beams is described where Rayleigh based distributed optical fiber sensors (DOFS) are implemented together with traditional electrical strain gauges for the monitoring of these elements during a three-point load test. Part of the DOF sensor is embedded without protective coating directly in the rebar inside the concrete, being the remaining fiber glued to the surface of the element after the concrete hardening. This allows the direct comparison between the developed strains on the surface of concrete and the rebar with the use of a single sensor. Moreover, two types of adhesives are studied and then compared. From all the possible distributed sensing techniques, the Rayleigh based Optical Frequency Domain Reflectometer (OFDR) is the one which enables the better spatial resolution without the need of post-processing algorithms. In this way, in this experiment, this is going to be the used sensing technique. [DOI]   -> Link to full text in repository
In the past decade, several works and studies have been performed with the goal of improving the knowledge and developing new techniques associated with the application of Distributed Optical Fiber Sensors (DOFS) in order to widen the range of applications of these sensors and also to obtain more correct and reliable data. In this document, after a very brief introduction to the fundamentals of this technology, the most representative work being developed at UPC—BarcelonaTech with the use of these sensors is going to be described. These applications range from laboratory experiments to real world structures monitoring scenarios where different challenges and particular issues had to be overthrown in each one of them. Furthermore, the most recent laboratory experiment performed by this group where DOFS were deployed is going to be described in greater detail. [DOI] -> Link to full text in repository
In the present paper, a novel technique is used to monitor and evaluate shear crack patterns in Partially Prestressed Concrete (PPC) beams. The proposed technique is based on experimental data obtained in two PPC beams tested in laboratory and instrument-ed by Distributed Optical Fiber Sensors (DOFS). The DOFS conform optical fiber grids bonded in the surfaces of the beams. The DOFS experimental data were obtained using an OBR (Optical Backscattered Reflectometer) system that provides continuous strain data with high spatial resolution and cracks can be characterized. The continuous (in space) monitoring of the strain along the DOFS, including the crossing of a crack provides additional information without requiring prior knowledge of the cracked zone.

Several experiences have demonstrated the feasibility of using OFDR theory and SWI technique in the structural monitoring of concrete structures (Villalba and Casas 2013, Rodriguez et al 2015). In the specific case of detection, location and control of cracking in concrete structures, OBR system is an attractive monitoring tool. In the evaluation of shear crack pattern, the inclination of the cracking pattern is an additional unknown property. Two PPC beams named I1 and I2, were tested using DOFS grids as measuring alternative to check the proposed structural monitoring method.

According to the preliminary results obtained in this paper, the use of DOFS is a feasible methodology to obtain important information in the study of shear structural behavior in concrete structures. Continuous strain data at different loading levels were obtained with high spatial resolution by OBR system. Using this data, detection and location of flexural and shear cracks were obtained without requiring prior knowledge of the cracked zone.

Finally, in the evaluation of the shear crack pattern, not only crack initiation and location are of importance, but also crack width, shear crack angles and shear sliding displacements along the cracks have to be measured to evaluate the shear performance of a structural element. -> Link to full text in repository

References.-
Villalba S. and Casas J. 2013. Application of optical fiber distributed sensing to health monitoring of concrete structures. Mechanical Systems and Signal Processing, 441-451.
Rodríguez G., Casas J., and Villalba S. 2015. Cracking assessment in concrete structures by distributed optical fiber. Smart Materials and Structures, 24, 1-11.

It’s widely recognized that during its lifetime, civil engineering structures are subjected to adverse changes that affect their condition and structural safety. These changes are due to several factors such as damage and deterioration induced by environmental aggressions, design and/or construction errors, overloading, not expected events such as earthquakes or simply due to the normal degradation associated with the normal use of the structure through their working life. In this way, the application of Structural Health Monitoring (SHM) systems to these civil engineering structures has been a developing studied and practiced topic, that has allowed for a better understanding of structures’ conditions and increasingly lead to a more cost-effective management of those infrastructures.

In this field, the use of fiber optic sensors has been studied, discussed and practiced with encouraging results. These sensors present several advantages when compared with the more traditional and used electric sensors, such as their immunity to electromagnetic interferences and corrosion, their ability to withstand high temperatures and their small dimensions and light weight just to name a few. Furthermore, with distributed fiber optic technology it’s possible to measure virtually any point along a single fiber allowing for truly distributed sensing measurements with great spatial resolution. The possibility of understanding and monitor the distributed behaviour of extensive stretches of critical structures it’s an enormous advantage that distributed fiber optic sensing provides to SHM systems. These distributed fiber optic sensors (DOFS) when bonded or embedded in the structural material works as its nervous system and for all these reasons, it is acknowledged as the most promising fiber optic sensing technique.

In the past decade, several R&D works have been performed with the goal of improving the knowledge and developing new techniques associated with the application of DOFS in order to widen the range of applications of these sensors and also to obtain more correct and reliable data. This paper presents, after a brief introduction to DOFS, the latest developments related with the improvement of these products as long as a review of their diverse applications on structural health monitoring with special focus on engineering structures. -> External link to publisher’s version -> Link to full text in repository

Two different existing structures monitored with distributed optical fiber sensors, are described in this paper. The principal Structural Health Monitoring (SHM) results of a valuable hospital rehabilitation (Sant Pau Hospital) and the enlargement of a prestressed concrete bridge (Sarajevo bridge), are presented. The results are obtained using a novel Distributed Optical Fiber Sensor system (DOFSs) based on an Optical Backscattered Reflectometry (OBR) technique. The versatility and easy installation of DOFSs compared with traditional monitoring systems is an important characteristic to consider its application in monitoring real world structures. The DOFS used in this study provide continuous (in space) strain data along the optical fiber with high spatial resolution in order of centimeters. Also and because the structural surfaces generally are roughness, the procedure to attach the optical fiber to two monitored structures are described. This is an important aspect because the influence in strain transfer between the DOFS and the surface is one of the principal parameters that should be considered in the application of the OBR technique.

Numerous works presenting information regarding the study of the potential of these sensors have been published in the last decade (Rodríguez et al. 2015 a,b; Palmieri & Schenato 2013) but very few showcase their application to real world structures. One of the various advantages of this technology is the easy installation to real-life structures and the variety of them that can be instrumented with it. In both studied instrumentations the used fiber is based on a type of fiber optic in which the wavelength is established and compatible with a commercial data acquisition system. Each section of optical fiber has a maximum length of 50 meters and the union between the fiber and structural element (concrete/masonry) was performed using a twocomponent type epoxy adhesive. A coating of a polymer (polyimide) was used to protect the fiber against scratches and environmental attack.

Due to their particularities, each one of these structures underwent changes in their structural behavior without, nevertheless, ceasing to serve their purpose, i.e. accommodating patients in the case of the Sant Pau Hospital and the passage of vehicles and pedestrians in the case of Sarajevo bridge thanks to the application of these sensors. With the results obtained in this work, the OBR theory associated with DOFS proved its reliability in SHM of civil engineering applications and continues to showcase the promising future of monitoring systems based on this technology. -> Link to full text in repository

References.-
Palmieri, L. & Schenato, L., 2013. Distributed optical fiber sensing based on Rayleigh scattering. The Open Optics Journal, 7(1).
Rodríguez, G., Casas, J.R. & Villaba, S., 2015. Cracking assessment in concrete structures by distributed optical fiber. Smart Materials and Structures, 24(3), p.35005.
Rodríguez, G., Casas, J.R.. & Villalba, S., 2015. SHM by DOFS in civil engineering : a review. Structural Health Monitoring and Maintenance, 2(4), pp.357–382.

In this paper, a structural health monitoring approach is proposed involving Laser Doppler Vibrometers (LDVs) installed on a vehicle. Relative velocities are measured to obtain the Rate of Instantaneous Curvature of the velocity (RIC). Standard deflection curvature is shown to be sensitive to local damage. Instantaneous Curvature (IC) is likewise sensitive but calculated using measurements provided from a vehicle. RIC is obtained using the first derivative of IC with respect to time.A damage indicator obtained from RIC, the Difference Ratio, is tested both in noise-free and noisy conditions.
Bridges play an important role in transport infrastructure and it is necessary to frequently monitor them. Current vibration-based bridge monitoring methods in which bridges are instrumented using several sensors are sometimes not sensitive enough. For this reason, an assessment of sensitivity of sensors to damage is necessary. In this paper a sensitivity analysis to bridge flexural stiffness (EI) is performed. A discussion between the use of strain or deflections is provided. A relation between deflection and stiffness can be set by theorem of virtual work, expressing the problem as a matrix product. Sensitivity is obtained by deriving the deflection respect to the reciprocal of the stiffness at every analysed location of the bridge. It is found that a good match between the deflection and the bridge stiffness profile can be obtained using noise-free measurements. The accuracy of sensors is evaluated numerically in presence of damage and measurement noise. Field measurements in the United States are also described to identify the potential issues in real conditions.
 The aim of this paper is to present the latest developments in the use of an instrumented vehicle called the Traffic Speed Deflectometer (TSD). A large axle load is applied to the pavement under the TSD. The deflection caused by this axle load is measured using several Doppler lasers. In the first step, the velocity of the deflection of the pavement is measured which can be shown to be proportional to the slope of the deformed profile. The pavement deflection is calculated in the second step using an integration model. A Winkler model is used to simulate the pavement behaviour under the axle load and the TSD is represented as a half-car model. The TSD is shown to be an effective tool for pavement damage detection. -> Link to full text in repository 

This paper develops a Bridge Weigh-In-Motion (BWIM) system which uses accelerometers instead of strain gauges to estimate the Gross Vehicle Weights (GVWs) of passing vehicles. The statistical properties of vehicles at a site (such as mean GVW) tend to be consistent so these properties can be a useful indicator of bridge condition. Conventional BWIM systems using strain gauges[1] are effective in finding traffic loads but strain gauges are not sensitive to bridge damage, except locally at the sensor location. Acceleration on the other hand, is influenced by damage at any location [2]. For this reason, the focus of this paper is the design of a BWIM system using accelerometers. To assess the feasibility of the system, the acceleration responses of a bridge at midspan are simulated for a single quarter car axle passing over it at different weights. It is shown that the system is substantially linear, i.e., amplitude is related linearly to axle weight. The concept of BWIM is to minimise the difference between measured and theoretical responses to applied load – inferred axle weights are those that minimise the sum of squared differences. The theoretical response is calculated as a linear combination of the responses to unit axle loads[3]. A vehicle bridge dynamic interaction analysis is carried out to simulate a population of 120 trucks crossing a typical bridge. The acceleration signals are extracted from these analyses. The BWIM algorithm is applied to infer the vehicle weights from these simulated ‘measured’ acceleration responses. The calculated axle weights are moderately accurate – classified as Class C according to the COST 323 Weigh-in-Motion classification system [1]. The relationship between axle weights and acceleration response is influenced by the bridge condition. It is therefore concluded that any change in bridge condition will manifest itself as an incorrect change in inferred vehicle weights.

References:

[1] Richardson, J., Jones, S., Brown, A., OBrien, E. and Hajializadeh, D. 2014. On the Use of Bridge Weigh-in-Motion for Overweight Truck Enforcement, International Journal of Heavy Vehicle Systems, 21(2): 83-104.[2] Ojio, T, Carey, C. H., OBrien, E. J., Doherty, C,Taylor, S. E. 2016. Contactless Bridge Weigh-in-Motion. Journal of Bridge Engineering, ASCE, 21(7): 04016032[3] Brien, E.J. Quilligan, M. and Karoumi, R. 2006. Calculating an Influence Line from Direct Measurements. Proceedings of the Institution of Civil Engineers: Bridge Engineering, 159(3): 31-34.

The aim of this paper is to present the latest developments in the use of an instrumented vehicle called the Traffic Speed Deflectometer (TSD). A large axle load is applied to the pavement under the TSD. The deflection caused by this axle load is measured using several Doppler lasers. In the first step, the velocity of the deflection of the pavement is measured which can be shown to be proportional to the slope of the deformed profile. The pavement deflection is calculated in the second step using an integration model. A Winkler model is used to simulate the pavement behaviour under the axle load and the TSD is represented as a half-car model. The TSD is shown to be an effective tool for pavement damage detection. -> Link to full text in repository

Among all the Structural Health Monitoring (SHM) recent methods found in literature, drive by monitoring has demonstrated to be promising for damage detection purposes, particularly in bridges. As curvatures can be derived from displacement measurements taken by this method, they can also be used for damage detection, which has already been successfully demonstrated. This paper describes the use of Instantaneous Curvature (IC) for that purpose. Once the absolute displacements of the bridge are measured, damage location and quantification can be obtained through IC when having a moving reference over a bridge. In this paper, a bridge is represented by a finite element model of a Euler-Bernoulli beam. A Half-Car model of a vehicle is used to represent a Traffic Speed Deflectometer (TSD), a drive-by monitoring vehicle. Damage is represented as a loss of stiffness in different parts of the bridge and 1 % measurement noise is added. A generic road profile is also considered. Healthy and damaged states of the bridge are compared in order to validate the method. -> Link to full text in repository
The Traffic Speed Deflectometer (TSD) is a vehicle incorporating a set of laser Doppler vibrometers on a straight beam to measure the relative velocity between the beam and the pavement surface. This paper describes a numerical study to see if a TSD could be used to detect damage in a bridge. From this measured velocity it is possible to obtain the curvature of the bridge, from whose analysis, it will be demonstrated that information on damage can be extracted. In this paper a Finite Element model is used to simulate the vehicle crossing a single span bridge, for which deflections and curvatures are calculated. From these numerical simulations, it is possible to predict the change in the curvature signal when the bridge is damaged. The method looks promising and it suggests that this drive-by approach is more sensitive to damage than sensors installed on the bridge itself. -> Link to full text in repository
Considerable effort has been dedicated in recent years to the development of bridge damage detection techniques. Recently, drive-by monitoring has become popular as it allows the bridge to be monitored without installing sensors on it. In this work, the Traffic Speed Deflectometer (TSD), which incorporates a set of laser Doppler sensors on a straight beam to obtain the relative velocity between the vehicle and the pavement surface, is modelled to obtain deflections on the bridge as the vehicle drives. From these deflections it is possible to obtain the curvature of the bridge, from which inferences on damage can be made. However, most of the time, the measurements taken by drive-by sensors are subject to a set of uncertainties or noise that can lead the damage detection procedure to either give false positives or to miss damage. For that reason, an analysis is needed in order to determine if these methods can work properly in uncertain or noisy environments. Moreover, as the road surface roughness affects the dynamic interaction between the vehicle and the bridge, this may also have an effect on the damage predictions. Hence, the goal of this paper is to study the sensitivity of curvature measurements to both the presence of environmental noise and the effect of the road surface roughness. -> Link to full text in repository
Drive-by monitoring has received increasing attention in recent years, as it has great potential useful for Structural Health Monitoring (SHM) applications. Although direct instrumentation of civil infrastructures has been demonstrated to be a way of detecting damage, it is also a very expensive method as it requires data acquisition, storage and transmission facilities on each bridge. Drive-by constitutes an alternative that allows the monitoring of a bridge without the necessity of installing sensors on it. In this numerical study, the vertical displacements of the bridge are used for damage detection purposes. The goal of this paper is to describe a model that can reproduce the vertical displacements of the bridge when a simulated vehicle is driving through and show how these displacements change with damage. Vertical displacements are calculated before and after damage, so that the sensitivity of the data to bridge damage can be determined.

A finite element (FE) model of a simply supported beam interacting with a moving half car is used in this study. Damage is represented as a loss of stiffness in several parts of the bridge. Vertical displacements are generated at a moving reference for healthy and damaged states, corresponding to vehicle location on the bridge. Two options are explored, the first axle and the second one, as the locations to fix the simulated sensor on the vehicle. -> Link to full text in repository

Although vehicles emissions have a very significant impact on CO2 emissions, there remains a level of uncertainty concerning the methodological assumptions and parameters to consider in the calculation of greenhouse gas (GHG) emissions coming from the use phase of road pavements (Trupia et al 2016). In fact, recent studies highlighted how existing models can lead to very different results and that because of this, they are not fully ready to be implemented as standard in the life-cycle assessment (LCA) framework (Santero et al 2011; Trupia et al 2016).

This study presents an innovative approach, based on the application of Machine Learning to ‘Big Data’, for the calculation of the use phase emissions of road pavements due to truck fleet fuel consumption. The study shows that the Machine Learning regression technique is suitable to analyse the large quantities of data, coming from fleet and road asset management databases effectively, assessing and estimating the impact of rolling resistance-related parameters (pavement roughness and macrotexture measurements) on the use phase in road pavement LCA.

Keywords: Fuel Consumption, GHG emissions, Pavement LCA, Big Data, Machine Learning

“Using truck sensors for road pavement performance investigation” is a research project within TRUSS, an innovative training network funded from the EU under the Horizon 2020 programme. The project aims at assessing the impact of the condition of the road pavement unevenness and macrotexture, on the fuel consumption of trucks to reduce uncertainty in the framework of life-cycle assessment of road pavements. In the past, several studies claimed that a road pavement in poor condition can affect the fuel consumption of road vehicles. However, these conclusions are based just on tests performed on a selection of road segments using a few vehicles and this may not be representative of real conditions. That leaves uncertainty in the topic and it does not allow road mangers to review the current road maintenance strategies that could otherwise help in reducing costs and greenhouse gas emissions from the road transport industry. The project investigated an alternative approach that considers large quantities of data from standard sensors installed on trucks combined with information in the database of road agencies that includes measurements of the conditions of the road network. In particular, using advanced regression techniques, a fuel consumption model that can take into consideration these effects has been developed. The paper presents a summary of the findings of the project, it highlights implications for road asset management and the road maintenance strategies and discusses advantages and limitations of the approach used, pointing out possible improvements and future work.
In the past, experimental studies have estimated the impact of road surface conditions on vehicle fuel consumption to be up to 5%. This, together with a review of the current maintenance strategies, could lead to a significant reduction of costs and GHG emissions from the road transport industry. However, this has been established in experiments using a limited number of instrumented test vehicles under carefully controlled conditions (e.g. steady speed, no gradient etc.) and for short test sections. What is less clear is the significance of these impacts on vehicle fleet fuel economy, under real driving conditions and at network level. This has recently gained more focus in the highway authority and research community as carbon footprinting of road maintenance plans has gained importance. Modern trucks are fitted with many sensors as standard and used to inform decisions on maintenance and driver training requirements in large fleets. However, much of the information produced could also be used in the measurement of how road condition influences performance in terms of vehicle operation. In particular, one research questions has not been sufficiently well answered: ‘what is the influence of road pavement roughness on truck fleet fuel consumption?’ The research aims to provide an answer to this questions to help prioritize pavement maintenance and design decisions with respect to user and environmental impacts. In particular, the research will develop a fuel consumption model that would help engineers in assessing the impact of road conditions on truck fleet fuel consumption. Some of the most innovative regression techniques, including random forests and artificial neural networks will be used for the purpose. It is expected that the developed tools will help in reducing uncertainties in the topic and extend the system boundaries of life-cycle carbon footprint of the current road maintenance strategies.
The paper presents a comparison of fuel consumption estimates computed using the HDM-4 model with measurements from a large database owned by truck fleet managers. The study considers data from three different types of truck and 1,645 vehicles in total for a case study in the United Kingdom (UK). Typical fuel consumption has been estimated using HDM-4 for light, medium and heavy trucks. Trucks were considered driving at 85km/h on a completely flat 1 km road segment. This has been compared to the average fuel consumption of the same types of trucks driving on the M18 motorway in England. The model has been configured based on a calibration of HDM-4 for the local conditions of the UK in 2011 (12). The paper shows that application of the model in the current state of calibration does not always predict correctly what actually happens in real conditions at local level. From the analysis of the information available from the fleet manager database, the paper tries then to address the differences between measurements and predictions of the model by making assumptions about the factors that impact the final results, aiming to develop a solution for more precise and reliable estimates. The authors suggest that, in order to use the model at a strategic level, a recalibration of the HDM-4 model and further analysis should be performed in the UK. Finally, the paper discusses the methodology used for developing HDM-4 (and similar models) and suggests possible alternatives for future work regarding the calibration of HDM-4 and modeling of the fuel consumption of road vehicles using ‘Big Data’.
This paper presents the application of three Machine Learning techniques to fuel consumption modelling of articulated trucks for a large dataset. In particular, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models have been developed for the purpose and their performance compared. Fleet managers use telematic data to monitor the performance of their fleets and take decisions regarding maintenance of the vehicles and training of their drivers. The data, which include fuel consumption, are collected by standard sensors (SAE J1939) for modern vehicles. Data regarding the characteristics of the road come from the Highways Agency Pavement Management System (HAPMS) of Highways England, the manager of the strategic road network in the UK. Together, these data can be used to develop a new fuel consumption model, which may help fleet managers in reviewing the existing vehicle routing decisions, based on road geometry. The model would also be useful for road managers to better understand the fuel consumption of road vehicles and the influence of road geometry. Ten-fold cross-validation has been performed to train the SVM, RF, and ANN models. Results of the study shows the feasibility of using telematic data together with the information in HAPMS for the purpose of modelling fuel consumption. The study also shows that although all the three methods make it possible to develop models with good precision, the RF slightly outperforms SVM and ANN giving higher R-squared, and lower error. -> Link to full text in repository

Considering data from 260 articulated trucks, with ∼12900 cc Euro 6 engines driving along a motorway in England (M18), the study first shows how different approaches lead to the conclusion that road pavement surface conditions influence fuel consumption of the considered truck fleet. Then, a multiple linear regression for the prediction of fuel consumption was generated. The model shows that evenness and macrotexture can impact the truck fuel consumption by up to 3% and 5%, respectively. It is a significant impact which confirms that, although the available funding for pavement maintenance is limited, the importance of limiting GHG emissions, together with the economic benefits of reducing fuel consumption are reasons to improve road condition (Zaabar & Chatti, 2010). [DOI] -> Link to full text in repository

In Europe, the road network is the most extensive and valuable infrastructure asset. In England, for example, its value has been estimated at around £344 billion and every year the government spends approximately £4 billion on highway maintenance (House of Commons, 2011).

Fuel efficiency depends on a wide range of factors, including vehicle characteristics, road geometry, driving pattern and pavement condition. The latter has been addressed, in the past, by many studies showing that a smoother pavement improves vehicle fuel efficiency. A recent study estimated that road roughness affects around 5% of fuel consumption (Zaabar & Chatti, 2010). However, previous studies were based on experiments using few instrumented vehicles, tested under controlled conditions (e.g. steady speed, no gradient etc.) on selected test sections. For this reason, the impact of pavement condition on vehicle fleet fuel economy, under real driving conditions, at network level still remains to be verified.

A 2% improvement in fuel efficiency would mean that up to about 720 million liters of fuel (~£1 billion) could be saved every year in the UK. It means that maintaining roads in better condition could lead to cost savings and reduction of greenhouse gas emissions.

Modern trucks use many sensors, installed as standard, to measure data on a wide range of parameters including fuel consumption. This data is mostly used to inform fleet managers about maintenance and driver training requirements. In the present work, a ‘Big Data’ approach is used to estimate the impact of road surface conditions on truck fleet fuel economy for many trucks along a motorway in England. Assessing the impact of pavement conditions on fuel consumption at truck fleet and road network level would be useful for road authorities, helping them prioritize maintenance and design decisions. -> Link to full text in repository

Experimental studies have estimated the impact of road surface conditions on vehicle fuel consumption to be up to 5% (Beuving et al., 2004). Similar results have been published by Zaabar and Chatti (2010). However, this was established testing a limited number of vehicles under carefully controlled conditions including, for example, steady speed or coast down and no gradient, amongst others. This paper describes a new “Big Data” approach to validate these estimates at truck fleet and route level, for a motorway in the UK. Modern trucks are fitted with many sensors, used to inform truck fleet managers about vehicle operation including fuel consumption. The same measurements together with data regarding pavement conditions can be used to assess the impact of road surface conditions on fuel economy. They are field data collected for thousands of trucks every day, year on year, across the entire network in the UK. This paper describes the data analysis developed and the initial results on the impact of road surface condition on fuel consumption for journeys of 157 trucks over 42.6km of motorway, over a time period of one year. Validation of the relationship between road pavement surface condition and vehicle fuel consumption will increase confidence in results of LCA analyses including the use phase. [DOI] -> Link to full text in repository
Bridges are essential components of rod network to connect different services at daily basic. Increasing freight and environmental impact cause deficiencies of bridge’s structures, which may cause catastrophic collapse. To prevent any sudden close of the bridge or extreme events, the bridges are often inspected in period time, for example, general inspection is carried on every two years. Although many methods have been developed to support to bridge inspection, the visual inspection with physic inspectors on the site is dominant. However, the visual inspection has many shortcomings: (1) subjective results; (2) requirement of heavy and/or special equipment; (3) traffic closure; (4) requirement of high-skill trained inspectors; (5) high risk for inspector; and (6) time consuming and expensive.

An alternative method can be using remote sensors, such as camera (Nishimura et al. 2012) for the inspection. However, these methods also have drawbacks: difficulty in acquiring details of an entire structure because of restriction of fixed view angles or associated occlusions problem. Recently, with development of robotics and computer vision, low-cost UAVs have been introduced to the market, and using UAVs for bridge inspection became a competitive method with many benefits such as non-contact measurement, no requirement of traffic close or any heavy/special equipment, no need trained engineers. Additionally, UAVs provide better data coverage, especially in hard to rich area like the bottom side of the deck or higher part of the bridge’s pylon.

Recent state-of-the-art of computer vision-based methods allow to generate accurate, high dense point cloud from UAVs-images with a single digital camera, which is often provided by laser scanning system. That can show UAVs’ ability in capture 3D topographic data of structures. This has accelerated the application of using UAVs for infrastructure inspections, such as building modeling (Byrne et al. 2017), dam inspection (Hallermann et al. 2015), and road surface evaluation (Distresses & Zhang 2012).

Focusing on bridge inspection, this study investigates the utility of the Structure from Motion (SfM) approach to generate a point cloud from low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). Next, the paper also present a workflow based point density to remove redundant points known as outlier data points mainly caused by water surface or terrain. To find an effective solution to resolve the outlier noise problem, two commonly used noise reduction methods, statistical based method and surface fitting method have been tested with various of parameter settings. It shows that, the SOR filter can efficiently remove most of the outlier noise in this situation. By searching 400 neighbors at each point, the FPR can achieve 2.54%, which means 97.46% of noise have been removed. But the accuracy and processing time can be further improved.

References:

Byrne, J. et al., 2017. 3D Reconstructions Using Unstabilized Video Footage from an Unmanned Aerial Vehicle 3D Reconstructions Using Unstabilized Video Footage from an Unmanned Aerial Vehicle. Journal of Imaging, 3, p.15.

Distresses, S. & Zhang, C., 2012. An Unmanned Aerial Vehicle-Based Imaging System for 3D Measurement of Unpaved Road. , 27, pp.118–129.

Hallermann, N., Morgenthal, G. & Rodehorst, V., 2015. Unmanned Aerial Systems ( UAS ) – Case Studies of Vision Based Monitoring of Ageing Structures. International Symposium Non-Destructive, (September), pp.15–17.

Nishimura, S. et al., 2012. Development of a hybrid camera system for bridge inspection. In Proc., 6th Int. IABMAS Conf., CRC Press. Stresa, Lake Maggiore, pp. 2197–2203. Available at: http://www.crcnetbase.com/doi/abs/10.1201/b12352-328

Imagery-based, three-dimensional (3D) reconstructions from Unmanned Aerial Vehicles (UAVs) hold the potential to provide a safer, more economical, and less disruptive approach for bridge inspection. This paper describes a methodology using a low-cost UAV to generate an imagery-based, dense point cloud for bridge deck inspection. Structure from motion (SfM) is employed to create a three-dimensional (3D) point cloud. Outlier data are removed through a density-based filtering method. Next,  the unsupervised learning algorithm k-means and an object-based region growing algorithm are compared for accuracy with respect to bridge deck extraction. Last, an automatic pavement evaluation method is proposed to estimate the deck’s pavement condition. The procedure is demonstrated through an actual case study, in which a 3D point cloud of 16 million valid points was generated from 212 images. With that data set, the region growing method successfully extracted the deck area with an F-score close to 95%, while the unsupervised learning approach only achieved 76%. In the last, to evaluate the surface condition of the extracted pavement, a polynomial surface fitting method was designed to evaluate and visualise the damages.

This paper introduces a three-dimensional reconstruction experiment based on a physical laboratory-based experiment on a brick wall. Using controlled shooting distances and angles, different images sets were captured and processed with a structure from motion-based technique, which can reconstruct 3D models based on multi-view, Two-Dimensional (2D) images. Those 2D geometries are shown to generate significant deformations within the resulting point cloud, especially where there were large angles (with respect the camera position and the wall’s normal direction) and at close distances to the wall’s surface. This paper demonstrates that by overlapping different flawed image sets, the deformation problem can be minimised. -> Link to full text in repository