实验和机器学习预测化学激活的RHA基于RAC的压力强度,使用SHAP和PDP分析
Ahmed A Alawi Al-Naghi1, Tariq Ali2, Inamullah Inam3
1Civil Engineering Department, University of Ha'il, 55476, Ha'il, Saudi Arabia. a.alnaghi@uoh.edu.sa.
Scientific reports
|December 4, 2025
概括
可持续混凝土使用化学活性大米灰 (RHA) 和回收混凝土聚合物 (RCA) 高达40%的造砂 (FS). 这种方法保持了高性能混凝土 (HPC) 的长期强度和抗酸性.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 可持续建筑 可持续建筑
背景情况:
- 对于可持续建筑材料的需求日益增长.
- 需要有效地将工业和农业废弃物用于高性能混凝土 (HPC) 的再利用.
- 挑战包括回收混凝土聚合物 (RCA) 的强度损失和可变的补充水泥材料性能.
研究的目的:
- 研究化学激活的米灰 (RHA),RCA和造砂 (FS) 对HPC的协同效应.
- 评估包含这些废物材料的HPC的压力强度和耐久性.
- 开发用于HPC性能的预测机器学习模型.
主要方法:
- 使用硫酸 (Na2SO4) 准备了六个实验HPC组,具有不同的RCA (0-100%) 和激活的RHA.
- 包括20%的FS作为细聚合物的替代品和所有混合物的恒定烟.
- 通过酸暴露测试评估了120天的压力强度和耐用性;应用了ML模型 (XGBoost,RF,ANN,KNN) 进行强度预测.
主要成果:
- 化学激活的RHA高达40%的RCA和20%的FS在长期强度和抗酸性方面没有妥协.
- 极端梯度增强 (XGBoost) 模型在预测压力强度方面取得了高精度 (R2=0.951).
- SHAP和PDP分析确定了固化年龄,RCA和Na2SO4含量作为影响HPC性能的关键因素.
结论:
- 使用激活的RHA和FS,可以有效地将高达40%的RCA纳入HPC,而不会损害性能.
- 该研究通过将实验数据与可解释的机器学习相结合,为可持续的混凝土设计提供了一个强大的框架.
- 这项研究促进了废物材料在高性能混凝土应用中的更广泛采用.
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