Parallel neural ordinary differential equation-based damage identification for building structures

Most existing data-driven-based methods for structural damage identification are based on black-box models without interpretability and explainability. In this study, a new parallel neural ordinary differential equation-based method is proposed for structural damage identification. The proposed method integrates the state-space equation-based physical model with a network block, which includes a group of parallel neural networks (NNs). The complex structural system is decomposed into physically interpretable subsystems, and each subsystem is represented by an independent NN. The group of parallel NNs is specifically designed to learn residual discrepancies between observed dynamic responses and predictions from the healthy structural system. The proposed approach explores the closed-form expression for a group of parallel NNs to identify the structural parameters, which increases the interpretability and enhances the reliability of the network model. A three-story building structure and a three-dimensional IASC-American Society of Civil Engineer benchmark frame are used to verify the performance of the proposed method. The results show that the proposed method is reliable and accurate to localise and quantify structural damage.