A Physics-Informed Deep Learning Framework for Structural Identification of Nonlinear Systems

This study introduces a novel physics-informed deep learning framework for structural identification of nonlinear systems under seismic excitation. The approach integrates Long Short-Term Memory (LSTM) networks with the governing equations of motion to estimate instantaneous, time-varying modal parameters (natural frequencies, damping ratios, mode shapes) without window-based approximations. A Convolutional Neural Network (CNN) extracts time-frequency features, enhancing the LSTM's ability to capture history-dependent nonlinear behavior. Training employs a composite loss function that enforces physical constraints, ensuring predictions adhere to structural dynamics. Validated using shake-table test data from a full-scale base-isolated building, results demonstrate exceptional accuracy in reconstructing acceleration and displacement responses. The identified time-variant properties enable high-fidelity hysteretic loop reconstruction and tangent stiffness analysis. Sensitivity studies confirm robustness to sensor sparsity and initial conditions. This physics-constrained methodology advances real-time structural health monitoring, offering potential for damage detection and forward response prediction.