使用SHAP PDP和ICE进行高强度玻璃粉混凝土的机器学习预测和可解释性分析
Muhammad Sarmad Mahmood1, Tariq Ali1, Inamullah Inam2
1Department of Civil Engineering, Swedish College of Engineering and Technology, Wah Cantt, 47080, Pakistan.
Scientific reports
|July 2, 2025
概括
这项研究使用机器学习 (ML) 来优化高强度玻璃粉混凝土 (HSGPC). XGB-GWO模型准确地预测了压力强度,突出了可持续的混凝土设计潜力.
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.
- 计算科学 计算科学
背景情况:
- 可持续补充混凝土材料 (SCM) 对高强度混凝土 (HSC) 至关重要,但它们的影响,特别是玻璃粉,需要进一步研究.
- 优化混凝土混合物设计以提高压力强度 (CS) 和可持续性仍然是建筑领域的一个关键挑战.
研究的目的:
- 使用机器学习 (ML) 模型评估高强度玻璃粉混凝土 (HSGPC) 的压力强度 (CS).
- 通过混合优化技术,提高HSGPC的预测准确度.
- 确定影响可持续设计的具体CS的关键参数.
主要方法:
- 编制了598点的数据集,参数包括水泥,玻璃粉,聚合物,水,超级塑化剂和固化日.
- 训练并比较独立的ML模型:K-最近邻居 (KNN),随机森林 (RF) 和极端梯度增强 (XGB).
- 使用粒子集群优化 (PSO),火虫算法 (FA) 和灰狼优化器 (GWO) 优化XGB,并使用SHAP,PDP和ICE分析进行参数影响.
主要成果:
- 在测试组件上,RF实现了R2 = 0.963和XGB实现了R2 = 0.946.
- 混合XGB-GWO模型表现出优异的性能,R2 = 0.991,平均平方误差 (MSE) 降低了82.82%.
- 超级可塑剂剂量,固化日和粗聚合物被确定为影响CS的最有影响力的参数.
结论:
- 机器学习驱动的优化为可持续的混凝土设计提供了一种高效,数据驱动的方法.
- XGB-GWO模型在预测HSGPC压力强度方面提供了高准确度.
- 优化材料比例可以带来高强度,环保混凝土,减少对环境的影响.
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