Structural Damage Detection Using Adversarially Calibrated Simulations and Deep Learning from Frequency-Domain Signals

Structural Damage Detection is an area that is becoming increasingly important as structure age and become more prone to failure. Early identification of these changes can lead to significant cost savings and potential damage reduction. Conventional data-driven methods typically require large datasets from both damaged and undamaged structural states, which can be difficult or even impossible to collect in real-world situations. Meanwhile, purely model-based techniques often face challenges in accounting for real-time environmental variations and the complexities of structural behavior. To address this limitation, the proposed methodology in this paper employs a hybrid system that utilizes structural models to generate training data for various structural scenarios, using a methodology based on the concepts of Generative Adversarial Networks to find the optimal excitation parameters for the model, aiming to produce response levels as close as possible to those obtained experimentally. This data serves as input for training algorithms to classify the structural condition based on the frequency information of temporal acceleration signals. The results show that the neural-based computational learning techniques are able to achieve efficiency rates above 99% in damage localization and almost 97% in severity estimation over 2 min-long experiments on a four-story lab-scale shear building.