Machine learning algorithms to damage detection under operational and environmental variability

TLDR: The unique contribution of this study is a direct comparison of the four proposed machine learning algorithms that have been reported as reliable approaches to separate structural conditions with changes resulting from damage from changes caused by operational and environmental variations.

Abstract: The goal of this paper is to detect structural damage in the presence of operational and environmental variations using vibration-based damage identification procedures. For this purpose, four machine learning algorithms are applied based on auto-associative neural networks, factor analysis, Mahalanobis distance, and singular value decomposition. A baseexcited three-story frame structure was tested in laboratory environment to obtain time series data from an array of sensors under several structural state conditions. Tests were performed with varying stiffness and mass conditions with the assumption that these sources of variability are representative of changing operational and environmental conditions. Damage was simulated through nonlinear effects introduced by a bumper mechanism that induces a repetitive, impacttype nonlinearity. This mechanism intends to simulate the cracks that open and close under dynamic loads or loose connections that rattle. The unique contribution of this study is a direct comparison of the four proposed machine learning algorithms that have been reported as reliable approaches to separate structural conditions with changes resulting from damage from changes caused by operational and environmental variations.