August 25, 2025Open Access

Research on Monitoring and Intelligent Identification of Typical Defects in Small and Medium-Sized Bridges Based on Ultra-Weak FBG Sensing Array

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Authors

XLXinyan Lin YZYichan ZhangHohai University YKYinglong KangWuhan University of Technology

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Implication

Experimental analysis reveals machine learning enhances damage identification in small and medium-sized bridges, suggesting improved monitoring approaches.

Key Points

Damage identification improves with the integration of machine learning, enabling rapid classification and diagnosis of bridge conditions.
Strain analysis using ultra-weak fiber Bragg grating reveals different damage patterns, enhancing recognition efficiency.
Observational study utilized experimental scenarios simulating bridge damage under load, providing insights for better monitoring.
Proposed methods highlight the potential for enhanced engineering applications in bridge maintenance and safety.

Cite This Study

Lin et al. (2025) studied this question.

synapsesocial.com/papers/68af7b1f7567bf4f94ff2af5 https://doi.org/10.20944/preprints202508.1761.v1

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Implication

Experimental analysis reveals machine learning enhances damage identification in small and medium-sized bridges, suggesting improved monitoring approaches.

Research on Monitoring and Intelligent Identification of Typical Defects in Small and Medium-Sized Bridges Based on Ultra-Weak FBG Sensing Array

Discussion

Member takes

Implication

Key Points

Cite This Study

Research on Monitoring and Intelligent Identification of Typical Defects in Small and Medium-Sized Bridges Based on Ultra-Weak FBG Sensing Array

Discussion

Member takes

Implication

Key Points

Cite This Study

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