高压物理学报2025，Vol.39Issue(8)：78-88,11.DOI:10.11858/gywlxb.20251024
基于神经网络的硬化水泥浆体等效强度预测
Prediction of Equivalent Strength of Hydrated Cement Paste Based on Neural Networks
摘要
Abstract
To optimize material performance and ensure the safety of engineering structures,it is essential to investigate the mechanical properties of cement hydration models with complex structures.This study aims to investigate the influence of the water-to-cement ratio and phase volume fractions on the equivalent mechanical properties of cement paste,particularly focusing on how these parameters influence the behavior of the material.A data-driven model is proposed to predict the mechanical performance of hydrated cement structures.Three-dimensional structural slices of Portland hydrated cement paste were created by utilizing the HYMOSTRUC 3D software.Subsequently,an automated batch-processing script coded in Python was applied to transform these slices into ABAQUS models.Tensile simulations were performed to determine the equivalent elastic modulus and equivalent strength of the structures.Based on the simulation results,a backpropagation prediction model was developed using a data-driven approach.Hyperparameter optimization of the model was performed using K-fold cross-validation to improve its generalization capability.Consequently,the trained neural network model demonstrates high accuracy in predicting the mechanical properties of hydrated cement structures.This approach not only ensures reliable predictions but also significantly reduces the complexity associated with traditional microscale material analysis methods.Overall,this study offers an efficient and robust solution for performance prediction of cement-based materials.关键词
硬化水泥浆体/有限元方法/神经网络/数据驱动方法/单轴拉伸Key words
hydrated cement paste/finite element method/neural network/data-driven approach/uniaxial tension分类
数理科学引用本文复制引用
宋敏,杨予舒,祝华杰,王志勇..基于神经网络的硬化水泥浆体等效强度预测[J].高压物理学报,2025,39(8):78-88,11.基金项目
国家自然科学基金(12272257) （12272257）
山西省基础研究计划青年项目(202303021222387,202403021222519) （202303021222387,202403021222519）
