Computational methods for wireless structural health monitoring of cultural heritages

Abstract

The structural health monitoring of cultural heritages is addressed in this paper. The arising inverse problem is solved through the Learning-by-Examples (LBE) paradigm, exploiting data collected by a Wireless Sensor Network (WSN). More in detail, low-cost and low-size sensing devices are spread over the scenario to be monitored, allowing environmental data as well as acceleration and vibration information to be acquired and processed by means of a Support Vector Machine (SVM) in order to detect the presence/absence of a damage in the monitored structure. The proposed approach has been preliminary validated in a laboratory-controlled environment, demonstrating promising performance.