使用混合机器学习建模和参数分析预测工业废弃物结合可持续混凝土的机械性能
Md Alhaz Uddin1, Md Habibur Rahman Sobuz2, Md Kawsarul Islam Kabbo3
1Department of Civil Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.
Scientific reports
|July 20, 2025
概括
这项研究利用机器学习的废物材料预测了混凝土的压力强度. XGBoost和决策树模型准确预测强度,有助于可持续的建筑实践.
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:
- 建筑行业正在寻求可持续的替代传统水泥,以减少对环境的影响.
- 工业废弃物材料在混凝土中具有作为补充固材料 (SCM) 的潜力.
- 使用SCM预测混凝土的压力强度对于材料选择和结构完整性至关重要.
研究的目的:
- 调查使用工业废物材料作为混凝土中的水泥替代品的可行性.
- 开发和评估机器学习模型,用于预测结合SCM的混凝土的压力强度.
- 确定影响混凝土压力强度的关键材料变量.
主要方法:
- 从各种出版物收集了441个关于混凝土混合物设计和压力强度的数据条目.
- 应用机器学习技术,包括光梯度增强 (LGB),极端梯度增强 (XGB) 和决策树 (DT).
- 使用混合模型 (XGB-LGB,XGB-DT),并使用SHAP和PDP进行变量重要性分析.
主要成果:
- XGBoost和决策树模型实现了高精度 (R2=0.928训练).
- 混合XGB-DT模型显示出平衡的性能 (R2=0.907训练,R2=0.785测试).
- 粗聚合物,超塑性剂,水,水泥含量和150-200公斤/立方米的地面颗粒高炉渣 (GGBFS) 显著影响压力强度.
结论:
- 机器学习模型,单一和混合,可以准确地预测混凝土的压力强度与工业副产品.
- 这些模型有助于建筑行业评估材料特性和优化混凝土混合设计的可持续性.
- 这些发现支持在混凝土中使用废物材料,有助于减少碳排放和环境影响.
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