A wavelet based drive-by bridge inspection system

McGetrick, Patrick; Kim, C. W.

doi:10.1201/b17063-89

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Several attempts have been made to detect bridge damage using indirect measurements Lederman et al, 2014;Li and Au, 2014;McGetrick and Kim, 2014a;Nguyen and Tran, 2010). Nguyen and Tran (2010) use a Symlet wavelet transform of the displacement response measured on a moving vehicle to identify the location of cracks in a bridge.…”

Section: Introductionmentioning

confidence: 99%

Application of empirical mode decomposition to drive-by bridge damage detection

O’Brien

Malekjafarian

González

2017

European Journal of Mechanics - A/Solids

115

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A new method is proposed in this paper for bridge damage detection using the response measured in a passing vehicle. It is shown theoretically that such a response includes three main components; vehicle frequency, bridge natural frequency and a vehicle speed pseudofrequency component. The Empirical Mode Decomposition (EMD) method is used to decompose the signal into its main components. A damage detection method is proposed using the Intrinsic Mode Functions (IMFs) corresponding to the vehicle speed component of the response measured on the passing vehicle. Numerical case studies using Finite Element modelling of Vehicle Bridge Interaction are used to show the performance of the proposed method. It is demonstrated that it can successfully localise the damage location in the absence of road profile. A difference in the acceleration signals of healthy and corresponding damaged structures is used to identify the damage location in the presence of a road profile. The performance of the method for changes in the transverse position of the vehicle on the bridge is also studied.

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Section: Introductionmentioning

confidence: 99%

Application of empirical mode decomposition to drive-by bridge damage detection

O’Brien

Malekjafarian

González

2017

European Journal of Mechanics - A/Solids

115

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“…The effectiveness of this method has been verified by several studies, such as references. 24,[40][41][42] In this experiment, different additional masses are employed to simulate different damage cases. The mass is added to the middle of each span.…”

Section: Bridge's Damagementioning

confidence: 99%

Drive-by bridge damage detection using Mel-frequency cepstral coefficients and support vector machine

Lin

Zhang

2023

Structural Health Monitoring

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Bridge damage detection using vibration data has been confirmed as a promising approach. Compared to the traditional method that typically needs to install sensors or systems directly on bridges, the drive-by bridge damage detection method has gained increasing attention worldwide since it just needs one or a few sensors instrumented on the passing vehicle. Bridge frequencies extracted from the vehicle’s vibrations can be good references for damage detection. However, extant literature considered mainly low-frequency responses of the vehicle, while the high-frequency responses that also contained the bridge’s damage information were often ignored. To fill this gap, this paper developed a damage detection approach that utilized both low and high-frequency responses of the passing vehicle. Mel-frequency cepstral coefficients (MFCCs) and support vector machine (SVM) were employed to classify damage severity. Firstly, the vehicle’s frequency responses are utilized as input features to train SVM models to identify the bridge’s condition. Then, to reduce dimensions of inputs and improve training efficiency, frequency responses are projected from the Hertz scale into the Mel scale, and two means using MFCCs are used to feed different SVM models. A laboratory experiment with a U-shaped continuous beam and a model car was used to verify the effectiveness of the proposed method. Results showed that high-frequency responses contain much information about the bridge’s conditions, and using MFCCs could apparently improve computational efficiency. The errors of damage detection when a heavy car was employed were within 5%.

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“…However, it is difficult to distinguish between different artificial damage scenarios in the field experiment for the test vehicle speed of 40 km/h (11.1 m/s). A pattern-adapted wavelet is formulated and found to be beneficial for damage localisation in theoretical and experimental investigations; however its formulation requires prior knowledge or accurate estimation of bridge damage discontinuities [69]. The vehicle's transverse position on the road is also highlighted as having a significant influence on the DI's sensitivity to damage level, which may have implications for such an approach in practice.…”

Section: Wavelet Transformmentioning

confidence: 99%

A Review of Indirect Bridge Monitoring Using Passing Vehicles

Malekjafarian

McGetrick

O’Brien

2015

Shock and Vibration

159

133

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Indirect bridge monitoring methods, using the responses measured from vehicles passing over bridges, are under development for about a decade. A major advantage of these methods is that they use sensors mounted on the vehicle, no sensors or data acquisition system needs to be installed on the bridge. Most of the proposed methods are based on the identification of dynamic characteristics of the bridge from responses measured on the vehicle, such as natural frequency, mode shapes, and damping. In addition, some of the methods seek to directly detect bridge damage based on the interaction between the vehicle and bridge. This paper presents a critical review of indirect methods for bridge monitoring and provides discussion and recommendations on the challenges to be overcome for successful implementation in practice.

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A wavelet based drive-by bridge inspection system

Cited by 10 publications

References 15 publications

Application of empirical mode decomposition to drive-by bridge damage detection

Application of empirical mode decomposition to drive-by bridge damage detection

Drive-by bridge damage detection using Mel-frequency cepstral coefficients and support vector machine

A Review of Indirect Bridge Monitoring Using Passing Vehicles

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