Detecting Damage Evolution of Masonry Structures through Computer-Vision-Based Monitoring Methods

Detecting the onset of structural damage and its progressive evolution is crucial for assessment maintenance built environment. This paper describes application a computer-vision-based methodology health monitoring to shake table investigation. Three rubble stone masonry walls, one unreinforced two reinforced, were tested under natural earthquake base inputs, progressively scaled up collapse. White noise signals also applied dynamic identification purposes. Throughout experiments, videos recorded, both white excitation environmental vibrations, with at rest. The preprocessed motion magnification algorithms analyzed through principal component analysis. frequencies walls detected their decay was associated accumulation. Results agreed those obtained from another measurement system available in laboratory consistent crack pattern development surveyed during tests. proposed approach proved useful derive information on deterioration properties, showing feasibility this real field applications.