Substructural damage detection in shear structures via ARMAX model and optimal subpattern assignment distance

Abstract

A novel substructural damage detection approach based on auto-regressive moving average with exogenous inputs (ARMAX) model and optimal subpattern assignment (OSPA) distance is proposed to locate and quantify the damages. Firstly, the shear structure is divided into independent substructures so that damage identification can be performed on each substructure modeling with multi-input multi-output (MIMO) model, which is promising for practical application and distributed structural health monitoring. Compared with the auto-regressive (AR) model, the ARMAX model structure involves not only the outputs but also the inputs and the disturbance dynamics, which help to improve the performance of modeling every substructure and gain the flexibility to handle the disturbance caused by environmental noises even the structural responses contain strong correlations under different excitations. The features of a linear dynamic system can be represented by its poles estimated from ARMAX model, transforming the damage detection into multi-target tracking of system poles. The OSPA distance is used as an innovative damage indicator of a function of the ARMAX model poles in this study, and is calculated by Hungarian algorithm. Experimental verifications were conducted to prove satisfactory damage detection. The application on nonlinear damage identification in a complex three-dimensional reinforced concrete structure shows the great potential of the proposed pole-based OSPA distance in multi-sensor information fusion of structural responses from different directions.