城市建筑2026，Vol.23Issue(18)：9-12,4.DOI:10.19892/j.cnki.csjz.2026.18.03
机器学习驱动的绿色混凝土强度预测与BIM可视化研究综述
A Review of Machine Learning-Driven Strength Prediction and BIM-Based Visualization for Green Concrete
摘要
Abstract
Green concrete has a complex composition,and its compressive strength is influenced by the coupled effects of multiple factors,including recycled aggregate replacement ratio,supplementary cementitious materials reactivity,and water-to-binder ratio,exhibiting strong nonlinear characteristics that make accurate prediction difficult using traditional empirical models.This paper systematically reviews the research progress of machine learning in green concrete strength prediction,and compares the predictive performance and applicability of three typical models:Back Propagation neural network(BPNN),Support Vector Regression(SVR),and XGBoost.The results indicate that ensemble learning models such as XGBoost demonstrate optimal performance on large-scale datasets.On this basis,the application status of the SHAP explainability analysis method in revealing the influence mechanisms of material parameters is elaborated,and the BIM-AI fusion-driven pathway for performance visualization is discussed.关键词
绿色混凝土/机器学习/可解释分析/BIM可视化Key words
green concrete/machine learning/explainability analysis/BIM visualization分类
建筑与水利引用本文复制引用
张帆,罗森林..机器学习驱动的绿色混凝土强度预测与BIM可视化研究综述[J].城市建筑,2026,23(18):9-12,4.基金项目
安徽省教育厅自然科学重点项目"数字化驱动BIM-AI融合:绿色建筑安全评估与混凝土性能预测创新研究"(2025AHGXZK30273) （2025AHGXZK30273）
