Bridge Impact Damage Localization Using Time-Frequency Coherence
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Solution Overview
Problem
Current methods struggle to accurately identify localized hidden cracks in bridges caused by ship impacts due to environmental interference, lacking effective detection for crack position and severity.
Innovation Solution
A method using a wall-climbing robot with sensors to acquire acceleration response signals, generating time-series matrices, and applying time-frequency coherence analysis to separate environmental variables, calculate local correlation coefficients, and plot coherence degradation factors to determine damage position and severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If global indicators such as pier-top displacement and rotation angle are used to assess ship-impact damage, then the overall damage can be determined, but the localized hidden cracks cannot be effectively detected and located
Solution Approach 1:
The patent divides the bridge pier into multiple monitoring sections along the vertical and horizontal directions, with acceleration sensors arranged in a grid pattern. This segmentation allows localized damage detection by analyzing vibration characteristics at specific positions rather than relying on global indicators, enabling identification of hidden cracks at precise locations.
Solution Approach 2:
The patent employs local correlation coefficients and time-frequency coherence analysis to examine vibration characteristics at specific locations. By calculating correlation coefficients between adjacent sensor measurements and analyzing coherence spectra, the method identifies localized damage zones with different vibration patterns from the surrounding undamaged areas.
2Reliability
If acceleration response signals are used to monitor bridge damage, then damage can be detected, but environmental factors interfere with distinguishing true damage characteristics
Solution Approach 1:
The patent extracts and separates environmental noise from true damage signals by using correlation analysis and coherence spectral entropy. The method calculates local correlation coefficients to identify patterns consistent with structural damage while filtering out random environmental variations, thereby isolating true damage characteristics from environmental interference.
Solution Approach 2:
The patent introduces time-frequency coherence analysis as an intermediary tool to distinguish between environmental factors and structural damage. By transforming acceleration signals into the time-frequency domain and calculating coherence spectra, the method creates a mediator that highlights genuine damage patterns while suppressing environmental noise through entropy-based filtering.
3Productivity
If existing impact damage identification methods are used, then general damage can be assessed, but the position and severity of cracks cannot be accurately located
Solution Approach 1:
The patent transitions from spatial-domain analysis to time-frequency-domain analysis by applying wavelet transforms and coherence spectral analysis. This dimensional transformation enables simultaneous identification of damage position, severity, and temporal characteristics, providing precise localization through frequency-time mapping rather than relying on single-point measurements.
Data Source
AI summary
The present disclosure provides a method, a system, and a medium for maintaining bridge structures susceptible to damage triggered by ship impact, and relates to the field of bridge health monitoring technology. If ship-bridge collision damage occurs, time-frequency coherence degradation factors at different positions of a time-series matrix, allows determination of an impact damage position and a damage degree based on a convex maximum point in the trajectory curve of the time-frequency coherence degradation factors. This method can effectively separate the influence of environmental factors and provide a fundamental methodological basis for establishing ship-bridge collision damage assessment technology.