使用机器学习方法增强混凝土的可持续性,以提高机械性能
Amir Khan1, Aneel Manan2, Muhammad Umar2
1College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518061, China.
Scientific reports
|July 2, 2025
概括
机器学习准确地预测回收混凝土聚合物 (RCA) 的强度,确定水与水泥的比率和水泥含量作为关键因素. 这有助于可持续建设,通过优化RCA在混凝土混合物中的使用.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 越来越多的环境问题需要可持续的建筑材料.
- 回收混凝土聚合物 (RCA) 为天然聚合物提供了可行的替代品,减少了浪费和资源枯竭.
- RCA混凝土的不一致性能阻碍了其广泛采用.
研究的目的:
- 开发机器学习 (ML) 模型来预测RCA混凝土的机械性能.
- 确定影响RCA混凝土压力强度 (Fc) 和裂纹抗拉强度 (STS) 的关键参数.
- 通过可靠的性能预测,支持RCA在建筑中的集成.
主要方法:
- 从现有文献中编制了583个RCA混凝土样本的综合数据集.
- 训练和评估了三个ML模型:极端梯度提升 (XGBoost),决策树和K-最近邻居 (KNN).
- 使用SHAP分析来确定特征对强度预测的重要性.
主要成果:
- XGBoost表现出卓越的性能,Fc的R2值为0.86,STS的R2值为0.88.
- 水与水泥的比率和水泥含量被确定为影响RCA混凝土强度的最重要因素.
- 决策树显示中等准确性,而KNN的预测能力有限.
结论:
- 机器学习模型,特别是XGBoost,提供了一种可靠的方法来预测RCA混凝土性能.
- 了解关键影响因素可以优化RCA混凝土混合设计,以提高可持续性.
- 这种方法通过为工程师提供预测工具,促进采用更绿色的建筑实践.
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