刚性路面混凝土的性能优化使用metakaolin处理RCA和烟与实验和基于机器学习的方法实验和机器学习的方法
Tariq Alqubaysi1, Inamullah Inam2, Muhammad Zeeshan Qureshi3
1Department of Civil Engineering, College of Engineering, Northern Border University, 73222, Arar, Saudi Arabia. Tariq.Alqubaysi@nbu.edu.sa.
Scientific reports
|December 24, 2025
概括
回收混凝土聚合物 (RCA) 和补充混凝土材料 (SCM),如甲和烟,提高了混凝土的机械强度和耐用性. 机器学习模型,特别是XGBoost,可以有效地预测和优化RCA混凝土性能,以实现可持续建筑.
科学领域:
- 土木和环境工程 土木和环境工程
- 材料科学 材料科学 材料科学
- 可持续建筑 可持续建筑
背景情况:
- 普通波特兰水泥 (OPC) 生产导致大量的二氧化碳排放 (全球6-8%).
- 回收混凝土聚合物 (RCA) 提供了一个可持续的替代品,但由于孔隙性和软砂,其机械性能较低.
- 补充性水泥材料 (SCM),如甲 (MK) 和烟 (SF) 可能会增强RCA混凝土.
研究的目的:
- 调查甲 (MK) 泥处理和烟 (SF) 添加在提高基于RCA的混凝土的机械强度和耐用性方面的有效性.
- 为了评估RCA混凝土的性能,使用不同的替换水平 (0-100%) 和SCM剂量 (2.5-10%).
- 利用机器学习 (ML) 模型来预测和优化RCA混凝土的压力强度.
主要方法:
- 对混凝土混合物的实验研究,具有不同的RCA替代水平和SCM (MK和SF) 剂量.
- 使用单向ANOVA进行统计分析,以验证改进的意义.
- 使用实验和文献数据开发和优化ML模型 (XGBoost,AdaBoost),通过K-fold交叉验证和灰狼优化器 (GWO) 进行超参数调整.
主要成果:
- 添加MK和SF显著提高了RCA混凝土的机械强度和耐久性,减轻了低档RCA的弱点.
- 统计分析证实了SCM和RCA调整的显著积极影响 (P <0.05).
- 极端梯度增强 (XGB) 模型在预测压力强度方面取得了很高的准确性 (R2=0.949训练,0.899测试),表现优于AdaBoost.
结论:
- 甲和二氧化烟雾有效地改善了回收混凝土聚合物的性能.
- 机器学习,特别是像XGBoost这样的组合方法,为优化RCA混凝土混合设计和预测性能提供了强大的工具.
- 该研究支持使用RCA,SCMs和ML建模来设计可持续的混凝土混合物,并提高RCA混凝土在路面应用中的结构寿命.
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