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

To address the challenge of efficiently identifying and providing early warnings for typical structural damages in small and medium-sized bridges during long-term service, this paper proposes an intelligent monitoring and recognition method based on ultra-weak fiber Bragg grating (UWFBG) array sensing. By deploying UWFBG strain-sensing cables across the bridge, the system enables continuous acquisition and spatial analysis of multi-point strain data. Based on this, a series of experimental scenarios simulating typical structural damages—such as single-slab loading, eccentric loading, and bearing detachment—are designed to systematically analyze strain evolution patterns before and after damage occurrence. While strain distribution maps allow for visual identification of some typical damages, the approach remains limited by reliance on manual interpretation, low recognition efficiency, and weak detection capability for atypical damages. To overcome these limitations, machine learning algorithms are further introduced to extract features from strain data and perform pattern recognition, enabling the construction of an automated damage identification model. This approach enhances both the accuracy and robustness of damage recognition, achieving rapid classification and intelligent diagnosis of structural conditions. The results demonstrate that the integration of the monitoring system with intelligent recognition algorithms effectively distinguishes different types of damage and shows promising potential for engineering applications.