Bridge damage detection based on vibration data: past and new developments

Overtime, bridge condition declines due to a number of degradation processes such as creep, corrosion, and cyclic loading, among others. Traditionally, vibration-based damage detection techniques in bridges have focused on monitoring changes to modal parameters. These techniques can often suffer to their sensitivity to changes in environmental and operational conditions, mistaking them 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 based on the S101 bridge is also presented to test the damage sensitivity to a chosen methodology.Peer ReviewedPostprint (published version

Performance assessment of vibration parameters as damage indicators for bridge structures under ambient excitation

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.Peer ReviewedPostprint (published version

A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions

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.Peer ReviewedPostprint (published version

Advancements of vibration based damage detection techniques for small to medium span bridges

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 utilisation of advanced computational methods, such as machine learning, pattern recognition and advanced data normalisation algorithms.Postprint (published version

Development of vibration-based parameters as damage sensitive features for bridge structures

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 chang-ing environmental and operational conditions and are generally limited by their methodology, which assumes linear structural behaviour and signal stationarity. The present paper presents a detailed overview of the de-velopment of alternative DSFs derived from vibration characteristics, focusing on their conception, damage sensitivity evaluation and performance robustness assessment. Initially, selected vibration parameters are out-lined before their damage sensitivity is determined on progressive damage test data obtained from a post-tensioned three-span bridge under ambient conditions using supervised machine learning techniques in con-junction 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 induced ex-citation. Finally, a comparative evaluation of the vibration-based DSFs is made against modal-based DSFs performance from the literature.Postprint (published version

Damage identification of bridge structures using the Hilbert-Huang Transform

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 applica-tions. 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 out-puts to successfully achieve damage identification in a real bridge structure subjected to a progress damage test under single vehicle excitation

Vibration-based, output-only damage identification of bridge under vehicle induced excitation

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

Vibration based damage detection techniques for small to medium span bridges: a review and case study

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.Postprint (published version

Noninvasive empirical methods of damage identification of bridge structures using vibration data

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 mod-el 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 al-ternative 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 sub-jected to ambient and vehicle induced excitation, respectively. Numerous non-modal vibration-based parame-ters are detailed and selected for the assessment of either the ambient or vehicle induced excitation data based on their inherent properties.Postprint (published version

Evaluation of the Hilbert Huang transformation of transient signals for bridge condition assessment

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 nonstationary 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.Peer ReviewedPostprint (published version