Structural Health Monitoring Based on Artificial Intelligence Algorithm and Acoustic Emission Analysis

In the online approach for Structural Health Monitoring (SHM) particular relevance can be assumed in the Artificial Intelligence algorithms. In fact, the real time structure condition evaluation must be performed by the fast analysis of the data provided by several sensors and devices. Among the SHM techniques the most attractive are based on the analysis of the Acoustic emission. In particular, in the paper are considered the acoustic emission naturally generated by the building material under stress. This signal is used in the paper to evaluate the input features for the Machine learning. The features of the acoustic emission signal used in the training of the machine learning techniques are based on the Gutenberg–Richter law, which expresses the relationship between magnitude and total number of earthquake events in a defined region and time interval.

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