Structural Damage Detection of Truss Bridge under Environmental Variability

Abstract

Focused on the effects of environmental and operational variability on the structures, a novel procedure for structura l linear and nonlinear damage detection is proposed based on the time series analysis and the higher statistical moments. The higher statistical moments of residual error of AR model, such as skewness and kurtosis, are then defined as the new damage-sensitive feature s to be a complimentary. Six integrated damage-sensitive features are further defined for vibration-based damage detection in terms of arithmetic and geometric mean of the residual errors. A series of experiments on a complicated truss bridge combined with a steel bridge plate have been conducted in laboratory. Damage was simulated by loosening the bolts of joints, and environmental variability wer e introduced by changing the shaker input level. 16 acceleration data of the bridge in each baseline and test state are measured and recorded for the structural damage detection. Based on these time series of acceleration data, the applicability of the proposed proced ure is evaluated. Some valued conclusions are made and discussions suggested as well.