Dynamic identification methods and artificial intelligence algorithms for damage detection of masonry infills

Abstract

The failure of non-structural components after an earthquake is among the most expensive earthquake-incurred damage, and may also have life-threatening consequences, especially in public buildings with very crowded facilities, because exposition is high and the risk increases accordingly. The assessment of existing non-structural components is particularly complex because in-depth in situ investigation is necessary to detect the presence of deficiencies or damage. This problem concerns interior and exterior partitions made of various materials (e.g., glass and masonry), as well as equipment and facilities in construction (building, industry, and infrastructure). Defining the boundary conditions of these components is of paramount importance. Indeed, external restraints (i) affect dynamic properties and, thus, the action experienced during an earthquake, and (ii) influence the capacity to detach the component before failure from the bearing structure (e.g., an infill wall connected to the main structural frame, or equipment connected to secondary structural members such as floors). The authors, therefore, conducted environmental vibration tests of an infill wall and refined a finite element model to simulate typical damage scenarios to be implemented on the wall. Selected damage scenarios were then artificially realized on the existing infill and further ambient vibration tests were performed to measure the accelerations for each of them. Finally, the authors used these accelerations to detect the damage by means of established OMA, as well as innovative machine learning techniques. The results showed that convolutional variational autoencoders (CVAE), coupled with a one-class support vector machine (OC-SVM), identified the anomaly even when the OMA exhibited limited effectiveness. Moreover, the machine learning procedure minimizes human interaction during the damage detection process.