Detecting Beam-Column Connection Failures in ASCE Phase II Simulated Benchmark Studies using a Two-Step Bayesian Structural Health Monitoring Approach

Abstract

This paper uses a two-step probabilistic structural health monitoring approach to analyze the unbraced cases in the Phase II simulated benchmark studies sponsored by the IASC-ASCE Task Group on Structural Health Monitoring. These cases involve damage detection and assessment of beam-column connection failures of the test structure using simulated ambient vibration data that is generated from the benchmark model which has randomly chosen structural parameter values. The two-step approach involves modal identification followed by damage assessment that uses the identified pre- and post-damage modal parameters based on a Bayesian updating methodology. An Expectation-Maximization algorithm is proposed to find the most probable values of the structural parameters. The results of analysis show that the probabilistic approach is able to successfully detect and assess damage locations for the full-sensor and the partial-sensor cases.