Development of Damage Detection Methodologies in Bridges Using Drive-by Methods and Machine Learning Algorithms: A Systematic Review of the Literature

Souza, Edson Florentino de; Bittencourt, Túlio Nogueira; Ribeiro, Diogo; Carvalho, Hermes

doi:10.1007/978-3-030-91877-4_15

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…On the other hand, this approach has a significant economic advantage when it comes to monitoring railway infrastructure. A single instrumented vehicle can provide information on a large track extension, making it a cost-effective solution for monitoring large railway stretches [6,7]. This particular approach is usually known as drive-by, vehicle scanning, or indirect monitoring and was first introduced in 2004 by Yang et al [8] for assessment of a bridge's natural frequency from the vehicle's response only.…”

Section: Introductionmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

et al. 2023

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Railways are one of the most important means of transportation, allowing people and goods to move quickly, environmentally beneficially, and efficiently over long distances. To ensure safe and reliable operations, regular condition-based assessments of trains and track are of paramount importance. Drive-by methodologies, which utilize data collected by onboard monitoring systems as the vehicle travels over the track, have gained popularity as an economically viable strategy for monitoring extensive track networks as well as vehicles traveling on them. This paper presents a critical review of these methodologies applied to railway tracks and vehicles. It assesses research on track irregularities, rail conditions, and the condition of rail supporting elements, highlighting important early developments and recent papers that provide insights into future practical applications. Additionally, the paper explores works related to global vehicle condition evaluation, focusing on the identification of suspension and wheelset element damage and also discussing challenges towards commercial application. The findings suggest that drive-by methodologies have several promising future applications. These include track maintenance optimization, proactive fault detection, predictive maintenance, track performance evaluation, vehicle health monitoring, and data-driven decision-making. By leveraging drive-by assessments, railway operators can optimize maintenance efforts, detect faults early, predict remaining component life, evaluate track performance, monitor vehicle health, and make informed decisions based on data analysis. Finally, a comprehensive conclusion summarizes the achievements thus far and provides perspectives for forthcoming developments. The future practical applications of drive-by methodologies discussed in this review have the potential to revolutionize railway track and vehicle assessments, leading to safer and more efficient railway operations in the days to come.

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Section: Introductionmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

et al. 2023

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“…Principal vibration signal processing techniques are based on time and frequency analysis, time-frequency analysis, and cepstral analysis [41,42]. Indirect damage detection was also studied, by utilizing mature technologies to process the vehicle responses, such as wavelet transform [30], frequency response function [43], and machine learning approach [44][45][46][47][48], among others. Deep learning algorithms have also been applied to the prediction of time-history responses of the bridge under vehicular loads with few and measurable input features extracted from VBI systems and vehicle responses to produce reliable training data [49].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges

et al. 2022

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In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) to extract the Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates the train, ballasted track and bridge behavior is presented. The formulation includes track irregularities and a damaged condition induced in a specified structure region. The feasibility of applying cepstrum analysis components to the indirect damage detection in HSR by on-board sensors is evaluated by numerical simulations, in which dynamic analyses are performed through a code implemented in MATLAB. Different damage scenarios are simulated, as well as external excitations such as measurement noises and different levels of track irregularities. The results show that MFCC-based DI are highly sensitive regarding damage detection, and robust to the noise. Bridge stiffness can be recognized satisfactorily at high speeds and under different levels of track irregularities. Moreover, the magnitude of DI extracted from MFCC is related to the relative severity of the damage. The results presented in this study should be seen as a first attempt to link cepstrum-based features in an HSR drive-by damage detection approach.

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Development of Damage Detection Methodologies in Bridges Using Drive-by Methods and Machine Learning Algorithms: A Systematic Review of the Literature

Cited by 2 publications

References 12 publications

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges

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