Bayesian Analysis of the Phase II IASC–ASCE Structural Health Monitoring Experimental Benchmark Data
Publication: Journal of Engineering Mechanics
Volume 130, Issue 10
Abstract
A two-step probabilistic structural health monitoring approach is used to analyze the Phase II experimental benchmark studies sponsored by the IASC–ASCE Task Group on Structural Health Monitoring. This study involves damage detection and assessment of the test structure using experimental data generated by hammer impact and ambient vibrations. The two-step approach involves modal identification followed by damage assessment using the pre- and postdamage modal parameters based on the Bayesian updating methodology. An Expectation–Maximization algorithm is proposed to find the most probable values of the parameters. It is shown that the brace damage can be successfully detected and assessed from either the hammer or ambient vibration data. The connection damage is much more difficult to reliably detect and assess because the identified modal parameters are less sensitive to connection damage, allowing the modeling errors to have more influence on the results.
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Information & Authors
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Published In
Journal of Engineering Mechanics
Volume 130 • Issue 10 • October 2004
Pages: 1233 - 1244
Copyright
Copyright © 2004 ASCE.
History
Published online: Oct 1, 2004
Published in print: Oct 2004
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