开箱机器学习模型用于使用XAI预测混凝土强度.
Sara Elhishi1, Asmaa Mohammed Elashry2, Sara El-Metwally2
1Department of Information Systems, Faculty of Computers and Information, Mansoura University, P.O. Box: 35516, Mansoura, 35516, Egypt. sara_shaker2008@mans.edu.eg.
Scientific reports
|November 14, 2023
概括
预测混凝土的强度对于建设至关重要. XGBoost机器学习模型实现了最佳性能,为工程师提供了洞察力,以优化混凝土混合设计和施工实践.
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:
- 高性能混凝土对于承受重负荷和极端天气的耐用基础设施至关重要.
- 准确的混凝土强度预测是优化施工性能,成本和安全的关键.
- 机器学习 (ML) 为结构工程挑战提供先进的解决方案,例如混凝土强度预测.
研究的目的:
- 评估和比较八个流行的机器学习模型的性能,以预测混凝土的强度.
- 根据混合物设计和加载条件确定最有效的ML算法来预测混凝土强度.
- 为土木工程师提供可操作的见解,使用ML用于具体的应用.
主要方法:
- 评估了八个ML模型:线性,,LASSO,决策树,随机森林,XGBoost,SVM和ANN.
- 使用1030个具体样本的标准数据集进行模型训练和测试.
- 为了模型的可解释性,使用了SHAP (夏普利添加式解释).
主要成果:
- 一个集体学习技术XGBoost表现出卓越的性能.
- 通过XGBoost模型实现了0.91的R-Square (R2) 和4.37的根平均平方误差 (RMSE).
- SHAP分析为XGBoost模型提供了有关特征重要性的见解.
结论:
- 集体学习方法,特别是XGBoost,对于具体的强度预测非常有效.
- 该研究为优化混凝土混合设计和施工实践提供了有价值的数据驱动的见解.
- 像XGBoost这样的ML模型可以显著提高土木工程项目的决策.
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