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基于实验和机器学习的红泥对回收聚合物的混凝土性能影响的分析

Imran Haidar¹, Tariq Ali², Muhammad Zeeshan Qureshi³

¹Department of Civil Engineering, University of Engineering and Technology, Taxila, Pakistan.

Scientific reports

|October 24, 2025

概括

本研究探讨了使用红泥 (RM) 和回收混凝土聚合物 (RCA) 的可持续混凝土. 10%的RM可以达到最佳压力强度,而机器学习可以准确预测性能.

科学领域:

材料科学与工程材料科学与工程
可持续的建筑材料可持续的建筑材料
混凝土技术混凝土技术

背景情况:

越来越多的环境问题需要在混凝土生产中找到可持续的替代方案.
工业副产品红泥 (RM) 和回收混凝土聚合物 (RCA) 为环保混凝土提供了潜力.
了解RM-RCA混凝土的机械性能和耐久性对于其实际应用至关重要.

研究的目的:

实验性地研究混凝土的压力强度和抗酸性,其中包含不同百分比的RM和RCA.
评估机器学习 (ML) 模型在预测RM-RCA混凝土压力强度方面的有效性.
使用SHAP分析识别影响ML模型预测的关键参数.

主要方法:

系统地用RM (0-15%) 替换水泥,用RCA (0-100%) 替换天然粗材.
对所有混凝土混合物的压力强度和抗酸性进行实验测试.
训练和验证使用251个数据集的四个整体ML模型 (RF,KNN,AdaBoost,XGBoost).

主要成果:

压力强度增加到10%的RM替换,在较高的RCA百分比下观察到显著的收益.
耐酸性最初改善,RM添加高达10%,然后下降.
XGBoost在预测混凝土压力强度方面表现出最高的准确性,固化年龄被确定为最有影响力的参数.

结论:

结合高达10%的RM和大量的RCA可以增强可持续的混凝土性能.
机器学习,特别是XGBoost,提供了一种可靠的方法来预测RM-RCA混凝土的性能.
固化年龄,RCA,烟 (SF) 和天然粗聚合物 (NCA) 是准确强度预测的关键因素.

关键词:

适应性提升 (AdaBoost) 是一个压力强度 (CS) 是指压力强度.极端梯度提升 (XGB) 的使用 K-最近的邻居 (KNN)机器学习 (ML) 是指机器学习.天然粗聚合物 (NCA) 是指天然的粗聚合物.随机森林 (RF) 是一个随机的森林.回收聚合物混凝土 (RAC) 的回收回收混凝土聚合物 (RCA) 的使用红色泥 (RM) 是指红色的泥.红泥混凝土 (RMC) 是一种红泥混凝土.沙普利添加剂解释 (SHAP)

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