物理编码数据驱动本构在堆石坝应力变形分析中的应用OA

In recent years,efforts have been made to apply the Artificial Intelligence for Science(AI4S)paradigm in various fields of hydraulic and hydropower engineering,e.g.the data-driven techniques used in the constitutive modeling of engineering materials.However,data-driven constitutive models often suffer from limited generalizability and robustness;most of the previous studies remained confined to simple numerical examples,leaving applicability to complex engineering problems in need of further verification.This study adopts a Generalized Plasticity Model-Physics-encoded Neural Network(GPM-PeNN),developed by our team,to simulate the stress and deformation of a rockfill dam.This model is trained using a synthetic dataset of rockfill materials from the Lawa high concrete-faced rockfill dam,and it is embedded into the general-purpose finite element code ABAQUS via a user-defined material module(UMAT).It is used to simulate the stress and deformation responses during dam-filling.Compared with finite element analyses based on traditional constitutive models,our simulations-based on the physics-encoded neural network constitutive model-align with general mechanical behaviors,and exhibit high accuracy and good convergence,thereby validating the feasibility of applying data-driven constitutive models in practical engineering applications.