Offline and online detection of damage using autoregressive models and artificial neural networks

Abstract

Developed to study long, regularly sampled streams of data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring. In this research, Autoregressive (AR) models are used in conjunction with Artificial Neural Networks (ANNs) for damage detection, localisation and severity assessment. In the first reported experimental exercise, AR models were used offline to fit the acceleration time histories of a 3-storey test structure in undamaged and various damaged states when excited by earthquake motion simulated on a shake table. Damage was introduced into the structure by replacing the columns with those of a thinner thickness. Analytical models of the structure in both damaged and undamaged states were also developed and updated using experimental data in order to determine structural stiffness. The coefficients of AR models were used as damage sensitive features and input into an ANN to build a relationship between them and the remaining structural stiffness. In the second, analytical exercise, a system with gradually progressing damage was numerically simulated and acceleration AR models with exogenous inputs were identified recursively. A trained ANN was then required to trace the structural stiffness online. The results for the offline and online approach showed the efficiency of using AR coefficient as damage sensitive features and good performance of the ANNs for damage detection, localization and quantification.