Khan Muhammad Adil, Ashraf Muhammad Shoaib, Onyelowe Kennedy C, Tariq Khawaja Adeel, Ahmed Mohd, Ali Tariq, Qureshi Muhammad Zeeshan
Civil Engineering Department, The University of Faisalabad, Faisalabad, Punjab, Pakistan.
Department of Civil Engineering, School of Engineering and Applied Sciences, Kampala International University, Kampala, Uganda.
Sci Rep. 2025 Mar 25;15(1):10255. doi: 10.1038/s41598-025-94387-2.
This study explores the potential of RCA combined with nano silica and chemically activated fly ash to produce sustainable and high strength concrete. The research addresses the challenges posed by RCA's inferior mechanical and durability properties by incorporating SCM. A comprehensive experimental program includes 420 and 240 samples for compressive strength and acid resistance. Machine learning algorithms such as Decision Trees, Random Forest, XG-Boost, and Ada Boost are used to predict RCA concrete performance metrics, with XG-Boost achieve the highest predictive accuracy (R = 0.995) for compressive strength while random forest performance is better for acid resistance (R = 0.909). The findings demonstrate substantial improvement in mechanical performance and durability, under scoring the effectiveness of SCMs in optimizing RCA- based concrete. The integration of machine learning provides a robust framework for performance predictions, contributing to the advancement of sustainable and resilient construction materials.
本研究探讨了再生粗骨料（RCA）与纳米二氧化硅及化学活化粉煤灰相结合以生产可持续高强度混凝土的潜力。该研究通过掺入补充胶凝材料（SCM）来应对再生粗骨料机械性能和耐久性较差所带来的挑战。一个全面的试验计划包括420个抗压强度样本和240个耐酸性样本。使用决策树、随机森林、极端梯度提升（XG - Boost）和自适应增强（Ada Boost）等机器学习算法来预测再生粗骨料混凝土的性能指标，其中极端梯度提升算法在抗压强度预测方面具有最高的预测准确率（R = 0.995），而随机森林算法在耐酸性预测方面表现更佳（R = 0.909）。研究结果表明，机械性能和耐久性有显著改善，突出了补充胶凝材料在优化基于再生粗骨料的混凝土方面的有效性。机器学习的整合为性能预测提供了一个强大的框架，有助于可持续和韧性建筑材料的发展。
