使用混合组合学习预测本托尼特塑料混凝土的可加工性和机械性能
Amir Tavana Amlashi1, Ali Reza Ghanizadeh2, Shadi Firouzranjbar3
1School of Civil and Environmental Engineering and Construction Management, University of Texas at San Antonio, San Antonio, USA. amir.tavanaamlashi@utsa.edu.
Scientific reports
|March 5, 2025
概括
这项研究引入了混合组合学习模型,使用法医基于调查优化 (FBIO) 进行了优化,以预测底塑料混凝土 (BPC) 的性能. 这些计算模型为土木工程材料的实验测试提供了经济有效的替代方案.
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
- 环境工程 环境工程
背景情况:
- 废水中的重金属污染需要高效且具有成本效益的整治解决方案.
- 班托尼特塑料混凝土 (BPC) 显示了重金属吸附的潜力,但由于成本和复杂性,它面临着实验验证挑战.
研究的目的:
- 开发准确的BPC可加工性和机械性能 (沉降,拉伸强度,弹性模量) 的预测模型.
- 克服BPC研究中传统实验方法的局限性.
- 提供一个用户友好的计算工具,用于在土木工程材料研究中实时应用.
主要方法:
- 采用了混合组合学习模型 (随机森林,自适应提升,极端梯度提升,梯度提升回归树).
- 模型使用法医基础调查优化 (FBIO) 进行了优化.
- 沙普利添加式解释 (SHAP) 分析被用于确定关键影响参数.
主要成果:
- 使用 FBIO (GBRT-FBIO) 优化的梯度增强回归树在预测弹性模量 (E) 中取得了最高的准确性.
- 与 FBIO (XGB-FBIO) 模型优化的极端梯度提升在预测拉伸强度 (TS) 和缩 (S) 上表现出卓越的性能.
- SHAP分析显示,水是沉的最重要因素,固化时间是拉伸强度和弹性模量最关键的因素.
结论:
- 使用 FBIO 优化的混合集体学习模型为预测 BPC 属性提供了可靠且具有成本效益的计算方法.
- 这些模型大大减少了对广泛而昂贵的实验测试的需求.
- 开发的在线工具促进了这些预测模型在土木工程中的实际应用,推动了材料研究.
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