混合元启发优化Catboost模型用于对混凝土固体板块的施工成本估计
Nanes Hassanin Elmasry1,2, Mohamed Kamel Elshaarawy3,4
1Civil Engineering Department, Faculty of Engineering, Tanta University, Tanta, 31733, Egypt.
Scientific reports
|July 2, 2025
概括
这项研究使用先进的AI模型增强了混凝土板块成本预测. 原子搜索优化与分类提升 (ASO-CatBoost) 结合,为建筑成本估计提供了卓越的准确性.
科学领域:
- 建设管理建设管理.
- 在工程领域的人工智能.
- 计算优化计算优化
背景情况:
- 准确的建筑成本预测对于竞争激烈的行业中项目成功至关重要.
- 传统的方法经常与成本因素的复杂性作斗争.
- 优化机器学习超参数对于预测准确性至关重要.
研究的目的:
- 开发和评估用于混凝土固体板块成本预测的先进混合模型.
- 使用元启发算法优化分类提升 (CatBoost) 模型的性能.
- 通过可解释的AI技术来识别影响成本的关键变量.
主要方法:
- 混合 CatBoost 与 Phasor 粒子群集优化 (PPSO),矮人蒙古斯优化 (DMO) 和原子搜索优化 (ASO) 的混合化.
- 对 CatBoost 模型进行超参数优化 (深度,学习速率,代).
- 使用剩余误差累积分布 (REC) 曲线,散射图,小提琴图和定量指标 (R2,RMSE) 的性能评估.
- 谢普利添加式解释 (SHAP) 用于变量重要性分析.
主要成果:
- 混合动力车型的性能明显优于独立的CatBoost车型.
- ASO-CatBoost实现了最高的精度,其R2为0.981和RMSE为1.222 $/m2.
- 根据SHAP的分析,支区和混凝土是最有影响力的成本预测因素.
- ASO-CatBoost模型展示了出色的概括能力.
结论:
- 优化的混合元启发模型提供了卓越的建筑成本预测准确性.
- ASO-CatBoost是一个非常有效的工具,用于预测混凝土固体板成本.
- 一个用户友好的Python GUI促进了开发的成本估计模型的实际应用.
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