Structural Damage Detection Based on Raw Monitoring Data

Abstract

At present, in most damage detection research, the raw structural monitoring data after secondary processing are usually used. To solve the problem, this paper used statistical process control to detect damage based on the raw measurement data. However, these data are measured at a rather high sampling frequency, which often leads to high correlation between them and eventually gives the error of damage detection. To minimize the negative effects, the statistical process control was realized by using the residual error between the original data and the predicted data of the autoregressive model, and the minimization problem is to obtain the appropriate order of the autoregressive model by taking the Aktan Information Criterion (abbreviation of AIC) as the objective function. The benchmark structure designed by International Association for Structural Control and American Society of Civil Engineers was taken as a case to prove the effectiveness of the proposed method.