使用可解释机器学习方法预测预先放置的聚合混凝土的强度.
Muhammad Faisal Javed1, Muhammad Fawad2,3, Rida Lodhi4
1Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, 23640, Pakistan. arbabfaisal@cuiatd.edu.pk.
Scientific reports
|April 10, 2024
概括
这项研究使用机器学习增强了预先放置的聚合混凝土 (PAC) 压力强度预测. XGBoost模型实现了高精度,识别了石,水与粘合剂的比率和超级塑化剂作为关键影响因素.
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:
- 前置聚合混凝土 (PAC),也称为两级混凝土 (TSC),是一种广泛使用的建筑材料.
- 复杂的PAC生产过程使其压力强度 (CS) 的准确预测变得复杂.
- 可靠的CS预测对于确保PAC在各种应用中的结构完整性和性能至关重要.
研究的目的:
- 提高PAC压力强度 (CS) 的理解和预测准确度,使用机器学习 (ML) 模型.
- 评估13种不同的ML模型的性能,以预测PAC CS.
- 通过敏感性分析确定影响PAC CS的最重要的输入参数.
主要方法:
- 通过使用261个数据点和11个输入变量的数据集,评估了13个机器学习模型.
- 应用XGBoost作为最适合的模型,用于使用SHAP (SHapley添加式扩展) 分析进行进一步分析.
- 开发一个图形用户界面 (GUI) 用于预测模型的实际应用.
主要成果:
- XGBoost模型在预测PAC压力强度方面表现出极高的准确性,达到0.9791的相关系数和0.9583.3的R2的相关系数.
- 渐变增强 (GB) 和CatBoost (CB) 也表现出强的表现.
- 灵敏度分析确定了石 (44.7%),沙子 (19.5%) 和水泥 (15.6%) 作为最有影响力的输入参数,而SHAP分析强调了水与粘合剂的比率,超级塑化剂和石是最重要的.
结论:
- 机器学习模型,特别是XGBoost,提供了一个高度准确和可靠的方法来预测预先放置的聚合混凝土的压力强度.
- 开发的GUI为土木工程师提供了一个实用的工具,可以利用ML模型在建筑项目中进行知情决策.
- 这项研究显著有助于提高预测模型在预置聚合混凝土强度预测领域的可靠性和适用性.
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