硅酸盐通报2025，Vol.44Issue(4)：1398-1407,10.DOI:10.16552/j.cnki.issn1001-1625.2024.1635
基于机器学习的碱激发矿渣-粉煤灰混凝土抗压强度与弹性模量影响因素分析
Analysis of Factors Influencing Compressive Strength and Elastic Modulus of Alkali-Activated Slag-Fly Ash Concrete Based on Machine Learning
摘要
Abstract
This study systematically investigated the complex factors influencing the mechanical properties of alkali-activated slag-fly ash(AASF)pastes and concrete through an integrated approach combining experimental investigations and machine learning techniques.The critical parameters governing compressive strength and elastic modulus were analyzed using random forest regression(RFR)and gradient boosting regression(GBR)models.The results show that the machine learning predictions demonstrate high accuracy,with deviations of compressive strength and elastic modulus predictions maintained within±15％of the experimental values.Quantitative predictive formulas are established to enhance the efficiency and effectiveness of mechanical performance optimization.A dual-objective analysis framework reveals synergistic relationships between compressive strength and elastic modulus in both paste and concrete systems,providing effective pathways for mix proportion optimization.The results demonstrate a threshold effect in fly ash content:positive correlation with compressive strength at content below 25％(mass fraction),transitioning to negative correlation when ranging between 50％and 75％(mass fraction).This research presents an efficient intelligent solution for performance optimization of AASF materials while establishing a practical foundation for low-carbon material development in civil engineering applications.关键词
碱激发材料/矿渣/粉煤灰/机器学习/抗压强度/弹性模量/双目标分析Key words
alkali-activated material/slag/fly ash/machine learning/compressive strength/elastic modulus/dual-objective analysis分类
土木建筑引用本文复制引用
刘琳,邵鑫,庞昆,郑蕻陈..基于机器学习的碱激发矿渣-粉煤灰混凝土抗压强度与弹性模量影响因素分析[J].硅酸盐通报,2025,44(4):1398-1407,10.基金项目
国家自然科学基金(52322805) （52322805）
