水力发电学报2026，Vol.45Issue(2)：46-57,12.DOI:10.11660/slfdxb.20260204
物理编码数据驱动本构在堆石坝应力变形分析中的应用
Application of physics-encoded data-driven constitutive modeling in stress-deformation analysis of rockfill dams
摘要
Abstract
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.关键词
数据驱动/本构模型/物理编码/堆石坝/应力变形分析Key words
data-driven/constitutive model/physics-encoded/rockfill dam/stress-deformation analysis分类
建筑与水利引用本文复制引用
贺志涵,马刚,周伟,汪泾周,李炎隆,胡锦方..物理编码数据驱动本构在堆石坝应力变形分析中的应用[J].水力发电学报,2026,45(2):46-57,12.基金项目
国家自然科学基金项目(52322907 （）
52579134 （）
U23B20149) （）
