An improved substructural damage detection approach of shear structure based on ARMAX model residual

In this paper, an improved substructure-based damage detection approach is proposed to locate and quantify damages in a shear structure, which extends from a previously established substructure approach. This method requires only three sensors to locate and quantify the damage in any story of a shear structure building. Similarly, as in the previous approach, a substructure approach is adopted in the improved procedure to divide a complete structure into several substructures. To improve the noise immunity and damage detection robustness under different types of excitations and realistic conditions, this paper proposes an autoregressive-moving average with exogenous inputs (ARMAX) model residual-based technique to correct the former damage indicator. The correction coefficient is defined as the normalized Kolmogorov-Smirnov (KS) test statistical distance between the two distinguished data sets of ARMAX model residual generalized from input-output data process for undamaged and damaged states. To better assess the performance of the improved procedure, simulation and experimental verifications of the proposed approach have been conducted, and the results are compared with the previous method. It shows that the improved procedure works much better and more stable than the previous method especially when it is applied to realistic problems.