Damage detection techniques for structural health monitoring of bridges from computer vision derived parameters

The paper presents damage detection techniques for structural health monitoring of bridges incorporating computer vision derived measurements. The feasibility of the techniques is demonstrated on a numerical model of a bridge girder. The girder is subjected to a load induced by a slowly moving truck. Multiple damage scenarios are simulated. Damage detection is carried out on the four types of response (i.e., deflection, inclination angle, strain and curvature) computed from the numerical model. The robustness of vision measurement approach for damage detection is validated at different levels of added measurement noise. The noise is expressed as the pixel resolution achievable with the image processing algorithm at multiple camera field of views applied to target motions. Damage detection and location accuracies are influenced by damage extent, added measurement noise and type of response. The study shows that deflections and strains outperform inclination angles and curvatures detecting damages in noisy measurements. Strains are the best type of response for damage detection and location when high measurement resolutions (e.g., 1/500 pixels) can be achieved.