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

作　　者：刘琳[1] 邵鑫 庞昆 郑蕻陈 LIU Lin;SHAO Xin;PANG Kun;ZHENG Hongchen(School of Civil Engineering and Transportation,Hohai University,Nanjing 210098,China)

机构地区：[1]河海大学土木与交通学院,南京210098

出　　处：《硅酸盐通报》2025年第4期1398-1407,共10页Bulletin of the Chinese Ceramic Society

基　　金：国家自然科学基金(52322805)。

摘　　要：本研究针对碱激发矿渣-粉煤灰(AASF)浆体和混凝土力学性能的复杂影响因素,采用试验与机器学习相结合的方法,系统分析了影响抗压强度和弹性模量的关键参数。通过随机森林回归(RFR)和梯度提升回归(GBR)模型对材料的抗压强度和弹性模量进行分析预测。结果表明,机器学习预测的材料抗压强度与弹性模量与真实值差距可以控制在±15%以内,并提供了定量计算公式,显著提升了对材料力学性能优化的效率和效果。基于双目标分析方法,进一步探讨了浆体和混凝土抗压强度与弹性模量之间存在的搭配组合关系,并揭示了优化配比设计的有效路径。粉煤灰掺量存在显著的阈值效应,当掺量小于25%(质量分数)时,与抗压强度呈正相关,当掺量介于50%~75%(质量分数),与抗压强度呈负相关。本研究为碱激发矿渣-粉煤灰材料的性能优化提供了一种高效、智能的解决方案,同时为土木工程领域的低碳材料研究提供了实践基础。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 

分 类 号：TU526[建筑科学—建筑技术科学]