Statistical time series methods have proven to be a promising technique in structural health monitoring, since it provides a direct form of data analysis and eliminates the requirement for domain transformation. Latest research in structural health monitoring presents a number of statistical models that have been successfully used to construct quantified models of vibration response signals. Although a majority of these studies present viable results, the aspects of practical implementation, statistical model construction and decision-making procedures are often vaguely defined or omitted from presented work. In this article, a comprehensive methodology is developed, which essentially utilizes an autoregressive moving average with exogenous input model to create quantified model estimates of experimentally acquired response signals. An iterative self-fitting algorithm is proposed to construct and fit the auto-regressive moving average with exogenous input model, which is capable of integrally finding an optimum set of auto-regressive moving average with exogenous input model parameters. After creating a dataset of quantified response signals, an unlabelled response signal can be identified according to a 'closest-fit' available in the dataset. A unique averaging method is proposed and implemented for multi-sensor data fusion to decrease the margin of error with sensors, thus increasing the reliability of global damage identification. To demonstrate the effectiveness of the developed methodology, a steel frame structure subjected to various bolt-connection damage scenarios is tested. Damage identification results from the experimental study suggest that the proposed methodology can be employed as an efficient and functional damage identification tool.

This article proposes a novel vibration-based damage identification method, named the probability distribution of decay rate. By introducing a statistical framework, the probability distribution of decay rate method estimates the damage-induced changes in overall damping behaviour of a free-vibration dynamic system. Utilising free-vibration impulse response data, a one-dimensional data set of local maxima–minima points is constructed. A statistical analysis of this data set is then performed to derive damage-sensitive parameters. It is demonstrated that through the use of a statistical analysis framework, a number of enhancements are attained in terms of both robustness and leniency in estimating the significantly nonlinear property of overall damping. An impact hammer test is conducted in the laboratory to verify the efficacy of the proposed probability distribution of decay rate method. The test was performed on a scale-model steel Warren-truss bridge structure, subjected to bolt-connection failures. The comparison results between the probability distribution of decay rate method and the standard experimental modal analysis method confirm that the former is effective for damage identification of complex structures, particularly with real experimental data and steel-frame structure assemblies.