Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification

Abstract

Currently, measurement points in bridge structural health monitoring are limited. Consequently, structural damage identification is challenging due to sparse monitoring data. Hence, a structural full-field displacement monitoring and damage identification method under natural texture conditions is proposed in this work. Firstly, the feature points of a structure were extracted via image scale-invariant feature transform. Then, the mathematical model was analyzed respecting the relative position change of the feature points before and after deformation, and a calculation theory was proposed for the structure’s full-field displacement vector (FFDV). Next, a test beam was constructed to obtain the FFDV calculation results for the beam under different damage conditions. Validation results showed that the maximum length error of the FFDV was 0.48 mm, while the maximum angle error was 0.82°. The FFDV monitoring results for the test beam showed that the rotation angle of the displacement vector at the damage location presented abnormal characteristics. Additionally, a damage identification index was proposed for the rotation-angle change rate. Based on the validation test, the index was proven to be sensitive to the damage location. Finally, a structural damage identification program was proposed based on the FFDV monitoring results. The obtained results will help to expand structural health monitoring data and fundamentally solve damage identification issues arising from sparse monitoring data. This study is the first to implement structural full-field displacement monitoring under natural texture conditions. The proposed method exhibits outstanding economic benefits, efficiency, and visualization advantages compared with the conventional single-point monitoring method.

Improved Blob-Based Feature Detection and Refined Matching Algorithms for Seismic Structural Health Monitoring of Bridges Using a Vision-Based Sensor System

“…Among the available detectors like edge or corner detectors, the blob-based detector is the simplest method that aims to analyze the shape features of an object in the image that contrast their backgrounds in color, brightness, or other properties. The SURF and SIFT algorithms are among the frequently used operators that can extract blob-based features in the image and have been implemented in bridge SHM [20,21]. Meanwhile, the KAZE algorithm is not commonly selected in bridge monitoring; however, previous work has reported its implementation in wind turbine monitoring [22].…”