Two-step Bayesian Structure Health Monitoring Approach for IASC-ASCE Phase II Simulated and Experimental Benchmark Studies

Abstract

This report uses a two-step probabilistic structural health monitoring approach to analyze the Phase II simulated and experimental benchmark studies sponsored by the IASC-ASCE Task Group on Structural Health Monitoring. The studies involve damage detection and assessment of the test structure using simulated ambient-vibration data and experimental data generated by various excitations. The two-step approach involves modal identification followed by damage assessment using the pre- and post-damage modal parameters based on the Bayesian updating methodology. An Expectation-Maximization algorithm is proposed to find the most probable values of the parameters. The results of the analysis show that the probabilistic approach is able to detect and assess most damage locations involving stiffness losses of braces in the braced frame cases, while the success of the approach in detecting rotational stiffness losses of the beam-column connections in the untraced cases may rely on sufficient prior information for the column stiffness.