Damage Detection in Presence of Varying Temperature Using Mode Shape and a Two-Step Neural Network

The dynamic characteristics of any structural system get affected not only due to damage but also from variations in ambient uncertainty. Thus, false positive or negative alarm may be signalled if temperature effects are not taken care off. The difficulty lies in correlating response measurements to corresponding damage patterns in the presence of varying temperature. This study employs machine learning algorithm to filter out the temperature effect from the measured mode shapes. A two-stage data-driven approach has been developed in which damage detection and localization are performed in consequence. For detection, a model to correlate mode shapes and temperature is formulated using an Auto-Associative Neural Network (AANN) and a temperature-invariant prediction error is defined as Novelty Index (NI). NIs are further classified to corresponding damage cases employing a fully connected layer network. With numerical experiments, the algorithm presented excellent efficiency and robustness against varying temperature in detecting damage.