Development of a damage detection system for expansion joints of highway bridges applying acoustic method

Nishikawa, Yoshiaki; Taniguchi, Koichi; Ichinose, Luiza Hiroko; Tsukamoto, Shigeaki; Yamagami, Tetsuji

doi:10.1201/b12352-17

Cited by 1 publication

(2 citation statements)

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“…Previous research has investigated the sound produced by vehicle impact on BEJs, with a focus on understanding its generation [16] and devising methods to mitigate it for environmental conservation [17,18]. Findings suggest that the audio response of BEJs contains information about their operational status, as corroborated by experienced inspectors who have leveraged these sounds to pinpoint anomalous BEJs [19]. Consequently, sound signals elicited by vehicle impact hold the potential for detecting BEJ damage.…”

Section: Introductionmentioning

confidence: 91%

“…According to research by Nishikawa [19], experienced inspectors can determine the fault status of BEJs by the sound of vehicle impact, indicating that vehicle impact is a reliable excitation source for audio-based fault detection of BEJs. Terefore, as shown in Figure 6, a cascading approach is established using three ConFormer models, each for a different VPR task, to ultimately achieve fault detection of BEJs.…”

Section: Cascading Approach For Bej Fault Detectionmentioning

confidence: 99%

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Fault Detection of In‐Service Bridge Expansion Joint Based on Voiceprint Recognition

Dong,

Wang,

Pan

et al. 2024

Structural Control and Health Monitoring

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Bridge expansion joints (BEJs) in service are susceptible to damage from various factors such as fatigue, impact, and environmental conditions. While visual inspection is the most common approach for inspecting BEJs, it is subjective and labor-intensive. In this paper, we propose a novel methodology for detecting the fault status of BEJs, inspired by voiceprint recognition (VPR) based on audio signals. We establish an Artificial Neural Network to filter nonevent segments from low signal-to-noise ratio signals, achieving an AuC value of 0.981. We design and improve ConFormer VPR models with a multifeature aggregation strategy and cascade them to realize fault detection of BEJs. For three successive tasks in classifying environment sound types, vehicle impact types, and faults, the ConFormer VPR models achieve AuC values of 0.975, 0.925, and 0.886, respectively, demonstrating the feasibility of our methods for unmanned inspection of BEJs. In future research, the introduction of multiple types of damage and the implementation of benchmarking tests are planned to further enhance the capabilities of the system.

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