Structural Damage Detection Based on Statistical Process Control

In most of the present research on damage detection, secondly processed raw monitoring data of structures are usually used. In order to deal with the above problem, damage detection was implemented based on the raw measured data using techniques of statistical process control (abbreviation of SPC) in this paper. However, those data are measured with rather high frequency of sampling, which often produces strong correlations of them and makes errors of damage detection finally. In order to alleviate those negative effects, techniques of SPC were carried out using residuals between raw data and predicted ones of autoregressive (abbreviation of AR) models, and furthermore the minimum problem was given with the objective function of Aktain's information criterion (abbreviation of AIC) to obtain appropriate orders of AR models. The Benchmark structure, proposed by International Association for Structural Control and American Society of Civil Engineers, was adopted as the case study to prove validity of the method proposed in the paper.