The integration of data‐driven methodologies in bridge health monitoring is gaining increasing attention due to their ability to overcome conventional approaches’ limitations. This study proposes a bridge damage identification method for simply supported bridges, leveraging output‐only vehicle‐induced vibration responses, eliminating the need for controlled excitation. The proposed methodology comprises four key phases: data acquisition (DAQ), feature extraction, feature discrimination, and development of a damage localization index. Acceleration responses from multiple vehicle passages are collected from different bridge states. Initially, baseline data is collected from the bridge in its current condition, which serves as a reference for comparison with future measurements. The method utilizes frequency domain higher‐order spectral moments as damage‐sensitive features (DSFs) to detect structural changes. The Mahalanobis distance ( Δ ) metric is employed as a feature discriminator between healthy and damaged states, leveraging its robustness to variable scaling and correlation structures. A new damage localization index, is introduced to localize the damage within the bridge. The method is evaluated using numerical simulations of a simply supported bridge where vehicle–bridge interaction (VBI) modeling is utilized to generate acceleration response data under different damage scenarios. Further, an experimental validation is conducted on a lab‐scale bridge. The results demonstrate that the proposed method successfully detects and localizes damage in both numerical and experimental settings.
Haque et al. (Thu,) studied this question.
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