A Bayesian Learning Method for Structural Damage Assessment of Phase I IASC-ASCE Benchmark Problem

Abstract

Rapid progress in the field of sensor technology leads to acquisition of massive amounts of measured data from structures being monitored. The data, however, contains inevitable measurement errors which often cause quantitative damage assessment to be ill-conditioned. The Bayesian learning method is well known to provide effective ways to alleviate the ill-conditioning through the prior term for regularization and to provide meaningful probabilistic results for reliable decision-making at the same time. In this study, the Bayesian learning method, based on the Bayesian regression approach using the automatic relevance determination prior, is presented to achieve more effective regularization as well as probabilistic prediction and it is expanded to provide vector outputs for monitoring of a Phase I IASC-ASCE simulated benchmark problem. The proposed method successfully estimates damage locations as well as its severities and give considerable promise for structural damage assessment.