Intelligent Hysteresis Modeling for Capturing Structural Features via Multi-Physics Guided Neural Network and Its Earthquake Resistant Test

The hysteresis model simplifies the relationship between resilience and deformation in seismic performance analysis of structural components. However, accurately capturing structural performance poses a challenge due to the intricate nature of hysteresis data. To enhance precision, this study introduces a multi-physics-guided neural network model. This innovative approach integrates explicit hysteresis models with an implicit network to create a robust hybrid model. The network architecture is grounded in physical principles, with the loss function incorporating physical parameter relationships for effective training. Through quasi-static testing, this approach demonstrates its robust adaptability and precision to the unique hysteretic curves of diverse components.