An artificial intelligence strategy to detect damage from response measurements: application on an ancient tower

Abstract

Automated modal identification procedures are attracting the interest of the Structural Health Monitoring (SHM) community as those techniques are capable of continuously providing information which are useful to timely assess the health state of a structure. Within this context, the paper presents the development and application of a vibration-based novelty detection strategy using automatically identified resonant frequencies and the Support Vector Machine (SVM) approach. The SVM is a popular technique for forming decision boundaries that separate data into two or more classes without any prior assumptions on the propriety of the data. The developed procedure is exemplified using frequency data collected during the continuous dynamic monitoring of a historic masonry tower that underwent slight permanent variation of the natural frequencies after the occurrence of a far-field earthquake. The obtained results highlight the capability of the novelty strategy to reveal slight damage and to detect anomalies in the structural behaviour.