Damage detection with small data set using energy-based nonlinear features

Summary This study proposes a new algorithm for damage detection in structures. The algorithm employs an energy‐based method to capture linear and nonlinear effects of damage on structural response. For more accurate detection, the proposed algorithm combines multiple damage sensitive features through a distance‐based method by using Mahalanobis distance. Hypothesis testing is employed as the statistical data analysis technique for uncertainty quantification associated with damage detection. Both the distance‐based and the data analysis methods have been chosen to deal with small size data sets. Finally, the efficacy and robustness of the algorithm are experimentally validated by testing a steel laboratory prototype, and the results show that the proposed method can effectively detect and localize the defects. Copyright © 2015 John Wiley & Sons, Ltd.