Artificial Intelligence based monitoring system for historical building preservation

The historical heritage requires particular systems to preserve its state of conservation. In this regard, the Structural Health Monitoring (SHM) systems they are fundamental in conjunction with suitable algorithms that allow the automatic detection of possible critical events that would ruin the state of conservation of the building. In this paper is proposed the use of a SHM system based on the analysis of the Acoustic emission in conjunction with an K-nearest-neighbor (KNN) Artificial Intelligence (AI) Algorithm for the classification of the data. Fundamental, in the use of the Classification algorithms based on AI, is the use of suitable features. In this regard, these features are estimated by using the Gutenberg–Richter law, typically used in the analysis of the earthquake. This permits to correlate the characteristic of the magnitude acoustic emission due to an event in the building with the number of the events. Experimental test will be used for the training and the test of the proposed architectures.