Structural damage detection and localisation using multivariate regression models and two-sample control statistics

Abstract

This paper presents model-free damage identification and localisation methods based on two-sample control statistics as well as damage-sensitive features to be extracted from single- and multivariate regression models. For this purpose, sequential normalised likelihood ratio test and two-sample t-test are adopted to detect the change in two families of damage features based on the coefficients of four different linear regression models. The performance of combinations of these damage features, regression models and control statistics are compared through a scaled two-bay steel frame instrumented with a dense sensor network and excited by impact loading. It is shown that the presented methodologies are successful in detecting the timing and location of the structural damage, while having acceptable false detection quality. In addition, it is observed that incorporating multiple mathematical models, damage-sensitive features and change detection tests improve the overall performance of these model-free vibration-based structural damage detection procedures.

Keywords::

• damage detection
• structural health monitoring
• statistical analysis
• regression models

Acknowledgements

Financial support of National Science Foundation and Commonwealth of Pennsylvania, Department of Community and Economic Development is gratefully acknowledged.

Notes

^1. Email: [email protected]

^2. Email: [email protected]

^3. Email: [email protected]

Additional information

Funding

Research funding is partially provided by the National Science Foundation [grant number CMMI-0926898] through Sensors and Sensing Systems Programme, and by a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA).