González, A., Barrias, A., Teixeira, R., Martínez, D., Heitner, B., Antonopoulou, S., Zou, G., González Merino, A., Sourav, S.-N., Casas, J.R., O’Connor, A., Nogal, M., OBrien, E.J., Yalamas, T., McNally, C., Banisoleiman, K., Costas, L. and Al-Sabah, S. (2018), 'TRUSS, a European innovative training network dealing with the challenges of an aging infrastructure network', in Proceedings of 7th Asia-Pacific Workshop on Structural Health Monitoring (APWSHM 2018), Hong Kong SAR, P.R. China, November 12-15.
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.
Gonzalez, A., Martinez, D., OBrien, E.J., Casero, M., Moughty, J.J., Casas, J.R., Vagnoli, M., Remenyte-Prescott, R., Andrew, J., Huseynov, F. and Brownjohn, J. (2018), 'TRUSS-ITN methods for detecting bridge damage from response to traffic', in Proceedings of 9th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2018), Melbourne, Australia, July 9-13.
González, A., Perrotta, F., Milana, G., Zou, G., Teixeira, R., González Merino, A., Sourav, S.-N. A., Antonopoulou, S., McNally, C., Al-Sabah, S., Costas, L., OConnor, A., Nogal, M., Banisoleiman, K., Faber, M.H., Parry, T. and Neves, L. (2018), 'Contributions by Marie Skłodowska-Curie TRUSS-ITN towards reducing uncertainty in structural safety of buildings, roads, energy and marine infrastructure', in Proceedings of 3rd International Conference on Vulnerability and Risk Analysis and Management + 7th International Symposium on Uncertainty Modelling and Analysis + 4th International Symposium on Uncertainty Quantification and Stochastic Modelling (ICVRAMISUMA2018), Florianópolis, Brazil, April 8-11.
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', in Proceedings of 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-8), Brisbane, Australia, December 5-8.
Antonopoulou, S., McNally, C. and Byrne, G. (2017). 'The effect of braiding parameters on the performance of braided BFRP composites for concrete reinforcement', in 8th International Conference on Composites Testing and Model Identification (CompTest2017), KU Leuven, Belgium, April 05-07. (Oral presentation)
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.
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
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
Teixeira, R., O'Connor, A.J., Nogal, M., Nichols, J. and Krishnan, N. (2017), 'Analysis of the design of experiments of offshore wind turbine fatigue reliability design with Kriging surfaces', in Proceedings of International Conference on Structural Integrity (ICSI2017), Funchal (Portugal), September 4-7, pp. 8.
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
Teixeira, R., O'Connor, A.J., Nogal, M., Nichols, J. and Spring, M. (2017), 'Structural probabilistic assessment of offshore wind turbine operation fatigue based on Kriging interpolation', in Proceedings of 27th European Safety and Reliability Conference (ESREL 2017), Portoroz, Slovenia, June 18-22.
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
Zou, G., Banisoleiman, K. and Gonzalez, A. (2018), 'Probabilistic maintenance optimization for fatigue-critical components with constraint in repair access and logistics', in Proceedings of the 14th International Conference on Probabilistic Safety Assessment and Management (PSAM14), Los Angeles, CA, USA, September 16-21.
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
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
Zou, G., Banisoleiman, K. and Gonzalez, A. (2017), 'Uncertainty quantification and calibration of a modified fracture mechanics model for reliability-based inspection planning', in Proceedings of the 12th International Conference on Structural Safety & Reliability (ICOSSAR 2017), Vienna, Austria, August 6-10.
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
Zou, G., Banisoleiman, K. and Gonzalez, A. (2017), 'Development of probabilistic fracture mechanics method for fatigue life prediction based on EIFS concept', in Proceedings of the ASEM 2017 36th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2017), Trondheim, Norway, June 25-30.
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
Huseynov, F., OBrien, E., Brownjohn, J., Faulkner, K., Xu, Y. and Hester, D. (2018), 'Monitoring crack movement on a masonry type abutment using optical camera system - A case study', in Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering (IALCEE 2018), Ghent, Belgium, October 28-31.
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.
Huseynov, F., Brownjohn, J.M.W., O’Brien, E.J. and Hester, D. (2016). 'Analysis of load test on composite I-girder bridge', in Proceedings of the CSHM-6: Structural health monitoring of new and ageing infrastructure, Queens University, Belfast, Northern Ireland, United Kingdom, May 26-27. (Selected within best papers)
Schoefs, F., Heitner, B., Yalamas, T., Causse, G. and OBrien, E. (2018), 'Using step-by-step Bayesian updating to better estimate the reinforcement loss due to corrosion in reinforced concrete structures', in Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering (IALCEE 2018), Ghent, Belgium, October 28-31.
Heitner, B., OBrien, E.J. Schoefs, F., Yalamas, T. and Causse, G. (2018), 'Multi-step bayesian updating of corrosion propagation in reinforced concrete structures', in Proceedings of 3rd International Conference on Vulnerability and Risk Analysis and Management + 7th International Symposium on Uncertainty Modelling and Analysis + 4th International Symposium on Uncertainty Quantification and Stochastic Modelling (ICVRAMISUMA2018), Florianópolis, Brazil, April 8-11. (Oral presentation)
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
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
Vagnoli, M., Remenyte-Prescott, R., Thompson, D., Andrews, J., Clarke, P. and Atkinson, N. (2017), 'A data mining tool for detecting and predicting abnormal behaviour of railway', in Proceedings of the 11th International Workshop on Structural Health Monitoring, Stanford, California, USA, September 12-14.
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
Moughty J.J. and Casas, J.R. (2017), 'Performance assessment of vibration parameters as damage indicators for bridge structures under ambient excitation', in Proceedings of 10th International Conference on Structural Dynamics (EURODYN 2017), Rome, Italy, September 10-13, Procedia Engineering Elsevier, vol. 199, pp. 1970-1975.
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
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
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.
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
Barrias, A., Casas, J.R., Rodriguez, G. and Villalba, S. (2017), 'UPC—BarcelonaTech experience on the use of Rayleigh based distributed optical fiber sensors for SHM of concrete structures', in Proceedings of 27th European Safety and Reliability Conference (ESREL 2017), Portoroz, Slovenia, June 18-22.
Rodríguez, G., Casas, J.R., Villalba, S. and Barrias, A. (2016), 'Monitoring of shear cracking in partially prestressed concrete beams by distributed optical fiber sensors' in Proceedings of the 8th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2016), Foz do Iguaçu, Brazil, June 26-30.
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
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.
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
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
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.
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 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 . 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. 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 . 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: 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. 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 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
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
Perrotta F., Parry T., Neves L.C. and Mesgarpour, M. (2018), 'A machine learning approach for the estimation of fuel consumption related to road pavement rolling resitance for large fleets of trucks', in Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering (IALCEE 2018), Ghent, Belgium, October 28-31.
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
Perrotta F., Parry T., Neves, L.C., Buckland T. and Mesgarpour M. (2018). 'Comparison of HDM-4 fuel consumption estimates with real measurements from trucks on motorways: a UK case study', in 97th Transportation Research Board Annual Meeting (TRB 2018), Washington, DC, USA, January 7-11. (Poster presentation)
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
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
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.
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
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