A Dempster-Shafer evidence theory-based approach for online structural health monitoring

Abstract

In this paper, a Dempster-Shafer evidence theory based approach for structural health monitoring is presented. Firstly, Bayesian method is employed to calculate the damage probabilities of substructures using each data set measured from the monitored structure, and the damage probabilities of substructures are transformed to damage basic probability assessments which used in evidence theory. Then the Dempster-Shafer evidence theory is employed to combine the individual damage basic probability assessments for getting the last damage detection results. With considering multi-sensors data including acceleration and strain, and measurement noise the numerical studies on a 14-bay planar rigid frame structure are carried out. The results indicate that the damage detection results obtained by combining the damage basic probability assessments from each test data are improved compared with the individual results obtained just by each test data separately.