Deep learning-based structural health monitoring of an ASCE benchmark building using simulated data

Abstract

Structural health monitoring (SHM) is essential for ensuring the safety and functionality of civil infrastructure. This study presents a deep learning-based approach to SHM in the ASCE benchmark building. To achieve this, the ASCE benchmark building is modelled in the ANSYS environment to simulate its response under various structural conditions, including both undamaged and multiple damaged states. The acceleration data obtained from these simulations is converted into scalogram images using the continuous wavelet transform. These images are employed to train two deep learning algorithms for structural state classification: the Convolutional Neural Network (CNN) and the Alex Net algorithms. Compared to Alex Net, the CNN algorithm excelled at detecting subtle damage patterns. Additionally, MobileNetV2 is employed to evaluate performance under limited data conditions, achieving better classification accuracy. This approach offers a valuable and automated tool for real-time damage identification and decision-making in SHM applications.