New Bayesian Model Updating Algorithm Applied to a Structural Health Monitoring Benchmark

Abstract

A new two-step approach for probabilistic structural health monitoring is presented, which involves modal identification followed by damage assessment using the pre and post-damage modal parameters based on a new Bayesian model updating algorithm. The new approach aims to attack the structural health monitoring problems with incomplete modeshape information by including the underlying full modeshapes of the system as extra random variables, and by employing the Expectation-Maximisation algorithm to determine the most probable parameter values. The non-concave non-linear optimisation problem associated with incomplete modeshape cases is converted into two coupled quadratic optimisation problems, so that the computation becomes simpler and more robust. We illustrate the new approach by analysing the Phase II Simulated Benchmark problems sponsored by the IASC-ASCE Task Group on Structural Health Monitoring. The results of the analysis show that the probabilistic approach is able to successfully detect and locate the simulated damage involving stiffness loss in the braces of the analytical benchmark model based on simulated ambient-vibration data.

Keywords

structural health monitoring Bayesian model updating damage detection probabilistic method ASCE benchmark

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