JJ Moughty

Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature. [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. [DOI] -> 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 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.

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 sorrounding 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

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 assessment of bridge condition from vibration measurements has generally been determined via the monitoring of modal parameters determined though 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 expressed 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 Brazil-ian 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.