A study on fabrications and vibration characteristics of steel finger joints simulating damage stages

Iwabuki, Hiroshi; Yabe, Akito; Ono, Shin-ichi; Tanaka, Shunsuke

doi:10.1201/9780429279119-320

Cited by 2 publications

(3 citation statements)

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“…(1) Te proposed methodology is a new endeavor to broaden the spectrum of inspection methods for BEJs. Te use of a consumer-grade microphone sensor in this study proves to be a cost-efective alternative when compared to more sophisticated NDT sensors such as accelerometers [8], ultrasonic sensors [11], and electromagnetic sensors [15] used in prior research. From a long-term perspective, the audio-based method ofers a more economical solution than manual inspections conducted by inspectors.…”

Section: Discussionmentioning

confidence: 94%

“…Accelerometers or displacement gauges are installed to capture the dynamic response of BEJs. Tis allows for the extraction of vibration characteristics induced by moving vehicles, changes which are detected for fault identifcation in damaged BEJ steel fngers [8]. A wireless installation, based on Internet-of-Tings technology, has also been proposed to enhance the fexibility of this approach [9,10].…”

Section: Introductionmentioning

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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Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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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“…One is intact and the other four are damaged. Three of the damaged finger joints were installed by Iwabuki et al [28] and these are of the same type as the intact one, and the damage stages (2) to (4) are simulated by intentionally omitting welding between the expective parts. In this study, these expansion joints are referred to as Intact, Slight-1, Slight-2, and Urgent Replacement Need 1 (U.R.N-1).…”

Section: Specimen Conditionsmentioning

confidence: 99%

Automatic Damage Detection of Bridge Finger-Type Expansion Joints Based on Video Object Detection and Sound Recorded on Vehicle

Imai¹,

Su²,

Mizutani³

et al. 2023

Preprint

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<p>Bridge finger type expansion joints are widely used because of their durability. However, they are susceptible to damage from fatigue and corrosion, and making the development of efficient damage detection methods crucial for preventing serious accidents. This study proposes a framework for automatic damage detection by simply driving in a vehicle, consisting of two steps: (1) detection of expansion joints by applying object detection to smartphone video, and (2) calculation of damage scores by passing sound recorded by an on-vehicle microphone. YOLOv5 object detection successfully detected 99.8% of expansion joints in the first step. Regarding the second step, experiments were conducted using one intact and four specimens modeling incremental damage. Finally, the damage score was proposed to quantify the sound of contact between among damaged structural members. After applying the proposed method to expansion joints that are in service, those identified as needing urgent replacement (Urgent Replacement Needed or U.R.N specimens) scored in the top 0.1% of the distribution of scores, indicating low false positives.</p>

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A study on fabrications and vibration characteristics of steel finger joints simulating damage stages

Cited by 2 publications

References 1 publication

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

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

Automatic Damage Detection of Bridge Finger-Type Expansion Joints Based on Video Object Detection and Sound Recorded on Vehicle

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