LAUSR

Abstract This study explored the sensor clustering-based damage detection beyond the free-vibration limitation to allow for the direct utilisation of time-series for damage identification under ambient vibration. In the proposed method, a dense sensor network is clustered and each sensor cluster is represented by nonlinear autoregressive with exogenous inputs (NARX) model, which is developed in a black-box manner via an artificial neural network. Damage detection is achieved through a new damage sensitive feature which is formulated from the NARX neural network prediction error. The efficiency of the proposed methodology is assessed firstly using test data of an 8-DOF system and later by conducting an experimental study on scaled steel arch laboratory models subjected to various damage cases. The obtained results reveal that the proposed method can satisfactorily detect, localise, and estimate damage severity in the test structure.