An overview of structural health monitoring and damage detection strategies: Fid-Bau Portal

An overview of structural health monitoring and damage detection strategies

Saadat, S. / Noori, M.N. / Buckner, G.D.

In recent decades, the growing number of civil, aerospace and other structures has accelerated the development of effective and accurate damage detection and health monitoring approaches. Many are based upon non-destructive and non-invasive sensing and analysis of structural characteristics. One category of health monitoring and damage detection strategies utilizes structural response information to identify the existence, location, and magnitude of strucural damage. Model based techniques seek to identify changes in the parameters of a linear dynamic model. These techniques include parametric and non-parametric modeling and system identification. Non-model based techniques focus on changes in the temporal and frequency characteristics of the response signals. Time-frequency analyses, such as wavelet transform, are a niche in quantitative approaches capable of detecting abrupt changes in structural response signals. In addition, there has been increasing interest in the use of artificial neural networks for reliable health monitoring and damage detection, which can potentially overcome the limitations of traditional model based and non-model based approaches. Artificial neural networks are typically utilized in two ways. The pattern recognition capabilities of neural networks allow for the identification of damage using response measurements from damaged and undamaged structures. The system identification capabilities of neural networks enable the estimation of dynamic parameters such as stiffness, mass, and damping. This paper presents a brief overview of health monitoring and damage detection strategies including the authors contributions.