Study on Machine Learning-based Prediction of Compressive Strength of Concrete with Different Waste Glass Powder Contents
YU Daidong1,2, MA Yuwei1,2,*, LI Gang1,2,*, WANG Aiqin1,2, HUANG Wei3, WANG Jingchao1,2
1 College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832003, Xinjiang, China; 2 Key Laboratory of Cold and Arid Regions Eco-Hydraulic Engineering, Xinjiang Production & Construction Corps, Shihezi 832000, Xinjiang, China; 3 Xinjiang Qiankun Engineering Construction Group, Tumushuke 843900, Xinjiang, China
Abstract: The application and promotion of waste glass powder concrete(WGPC) cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution. Compressive strength(CS) is a critical parameter for evaluating the efficacy of WGPC. Unlike conventional testing methods, machine learning techniques offer precise and reliable predictions of concrete's compressive strength, especially in its long-term mechanical properties. In this work, four models, namely Multiple Linear Regression(MLR), Back Propagation Neural Network(BPNN), Support Vector Regression(SVR), and Random Forest Regression(RFR) were employed. Furthermore, particle swarm optimization(PSO) algorithm and cross-validation techniques were applied to fine-tune the model parameters, striving for peak prediction performance. The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts. Notably, the PSO-RFR model excels among all evaluated models, showcasing superior performance on the testing dataset. It achieves a coefficient of determination(R2) of 0.923 1, a mean absolute error(MAE) of 2.107 3, and a root mean square error(RMSE) of 3.690 3. When compared to experimental results, the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy. Notably, the PSO-BPNN model exhibits the closest R2 values between its training and test sets. This close alignment of R2 values between the training and testing sets reflects the PSO-BPNN model's superior generalization ability for unseen data. The findings present an efficient method for predicting concrete's compressive strength, contributing to the sustainable development of concrete materials, and providing theoretical support for their research and application.
通讯作者: *Yuwei Ma,master's degree,associate professor,the research direction is in the field of new-type building materials.myw819@shzu.edu.cn Gang Li,correspording author,Ph.D.,professor,the research direction is in the field of new-type building materials.gangli@shzu.edu.cn
作者简介: Daidong Yu,master's degree student at the College of Water Conservancy & Architectural Engineering,Shihezi University,the research direction is in the field of new-type building materials.
引用本文:
于代东, 马玉薇, 李刚, 王爱芹, 黄维, 王靖超. 基于机器学习方法对不同掺量废玻璃粉混凝土抗压强度的预测研究[J]. 材料导报, 2026, 40(6): 25030056-15.
YU Daidong, MA Yuwei, LI Gang, WANG Aiqin, HUANG Wei, WANG Jingchao. Study on Machine Learning-based Prediction of Compressive Strength of Concrete with Different Waste Glass Powder Contents. Materials Reports, 2026, 40(6): 25030056-15.
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2026, Vol. 40 
