Structural Health Monitoring with Artificial Neural Network and Subspace-Based Damage Indicators

In recent years, different structural health monitoring (SHM) systems have been proposed to assess the actual conditions of existing bridges and effectively manage maintenance programmes. Nowadays, artificial intelligence (AI) tools represent frontier research providing innovative non-invasive non-destructive evaluations directly based on output-only vibration measures. This is one key aspects smart structures future. current study, an neural network (ANN) method has in order perform damage detection subspace-based indicators (DIs) other statistical indicators. A numerical case study example analysed with simulated damaged conditions. Based a comparison between reference situation new one, greatest advantage adopting these particular DIs because they are able point out significant changes, i.e. possible damage, without requiring beforehand modal identification procedure, which may introduce further noise modelling errors inside traditional process.