Fusing damage-sensitive features and domain adaptation towards robust damage classification in real buildings

Structural Health Monitoring (SHM) enables the rapid assessment of structural integrity in the immediate aftermath of strong ground motions. Data-driven techniques, often relying on damage-sensitive features (DSFs) derived from vibration monitoring, may be deployed to attribute a specific damage class to a structure. In practical applications, individual features are sensitive to specific levels of damage, and therefore combining multiple DSFs is required to formulate robust damage indicators. However, the combination of DSFs typically involves empirical thresholds that are often structure-specific and hinder generalization to different structural configurations. This work evaluates the predictive performance of a large ensemble of DSFs, computed on an extensive dataset of nonlinear simulations of frame structures with varying geometrical and material configurations. Gradient-boosted decision trees and convolutional neural networks are deployed to fuse multiple DSFs into damage classifiers, improving the predictive accuracy compared to best-practice methods and individual DSFs. A Domain Adversarial Neural Network (DANN) architecture enables the transfer of knowledge obtained from numerical simulations to real data from a large-scale shake-table test. After exposure to limited data, exclusively from the healthy state, the DANN framework yields satisfactory performance in predicting unseen damage states in the experimental data. The results demonstrate the potential of DANN in transferring knowledge from simulations to real-world monitoring applications, where only limited data characterizing exclusively the current, typically healthy, structural state is available. Overall, this work comprises the definition of multiple DSFs, their fusion through ML approaches, and the generalization of the knowledge obtained from simulations to real data through domain adaptation.