Damage detection of a truss bridge utilizing a damage indicator from multivariate autoregressive model

Abstract

This study proposes a damage indicator automatically derived from a set of multivariate autoregressive models estimated from ambient vibration of bridges. The damage indicator evaluates a stochastic distance between a set of reference data (healthy bridge data) and unknown test data. Statistical hypothesis testing based on a probability distribution of the damage indicator was applied for damage detection. A field experiment conducted on an actual steel truss bridge with truss members that were artificially severed was conducted to assess the damage detection efficacy of the proposed damage indicator. Experimentally obtained results showed that the proposed damage indicator enables detection of three damage patterns. The proposed damage indicator effectiveness was also assessed by comparison to the damage sensitive feature from a univariate autoregressive model using experimental data of the same bridge. This comparison also demonstrated the efficacy of the proposed damage indicator obtained from a multivariate linear system.