Statistical structural damage detection with consideration of temperature variation

Abstract

Structural responses vary with changing environmental conditions, particularly temperature. The variation in structural responses caused by temperature changes may mask the variation caused by structure damages. A data fusion-based damage detection approach under varying temperature conditions is presented in the paper. First, the temperature effect on the vibration properties is eliminated based on a linear regression model without losing damage information. Second, vibration properties are used to detect the structural damages with the Bayesian technique. Different sets of data measured at different times may lead to inconsistent monitoring results due to the uncertainties involved. The Dempster-Shafer evidence theory is employed to integrate the individual damage detection results and obtain a consistent one. An experiment on a two-story portal frame is investigated to demonstrate the effectiveness of the proposed method with consideration of the model uncertainty, measurement noise, and temperature effect. It shows that the damage detection results obtained by combining the damage basic probability assignments from each set of test data are more accurate than those obtained from each test data separately, and elimination of the temperature effect on the vibration properties can increase the damage detection accuracy.