对各种机器学习算法的比较分析,以预测含有废造砂的可持续绿色混凝土的强度特性
Muhammad Faisal Javed1,2, Majid Khan3, Muhammad Fawad4,5
1Department of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Swabi, 23640, Pakistan.
Scientific reports
|June 25, 2024
概括
机器学习模型准确地预测了废弃造砂的混凝土特性,SVR-GWO混合模型显示出了非常高的准确性. 这种方法可以促进可持续的混凝土生产和减少废物.
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:
- 废造砂 (WFS) 为减少混凝土生产中的废物提供了一个环保的解决方案.
- 在混凝土中实验测试WFS是资源密集型和耗时的.
- 准确预测混凝土性能对于优化WFS利用至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测混凝土的特性,其中包括废砂 (WFSC).
- 为了比较单个,混合和整体机器学习模型的预测性能.
- 确定影响WFSC属性和最佳混合设计的关键因素.
主要方法:
- 使用了支持向量回归 (SVR),决策树 (DT) 和AdaBoost回归器 (AR) 模型.
- 通过将SVR与优化算法 (火算法,粒子群优化,灰狼优化) 结合起来,构建了混合模型.
- 沙普利添加物扩展 (SHAP) 用于模型解释和特征意义分析.
主要成果:
- SVR-GWO混合型实现了高精度,压力强度 (CS) 和弹性模量 (E) 的R值为0.999,分裂拉伸强度 (STS) 的R值为0.998.
- 该AdaBoost回归组合模型显示了可比的预测准确性.
- 确定混凝土年龄是影响WFSC属性的重要因素,SHAP分析表明了最佳输入变量含量.
结论:
- 混合和整体机器学习模型为WFSC属性提供了比单个模型更高的预测准确性.
- 这些先进的计算技术可以显著降低实验成本,并加速WFS在可持续混凝土中的采用.
- 这些发现支持在环保建设实践中使用WFS,促进废物利用.
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