机器学习方法用于水泥稳定磁铁矿石和血矿铁矿石尾矿的弹性模块建模
Farzad Safi Jahanshahi1, Ali Reza Ghanizadeh2
1Department of Civil Engineering, Sirjan University of Technology, Sirjan, 7813733385, Iran.
Scientific reports
|February 10, 2025
概括
机器学习模型准确地预测了水泥稳定铁矿石尾矿的弹性模量 (Mr). 高斯过程回归显示出卓越的性能,确定水泥含量是最有影响力的因素.
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
- 地质技术工程 地质技术工程
背景情况:
- 弹性模量 (Mr) 对于灵活的路面设计至关重要,它代表了性和应力-延展性质.
- 通过动态三轴测试进行Mr的实验室测量是资源密集的.
- 铁矿石尾矿是潜在的建筑材料,但它们的机械性能需要彻底评估.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测水泥稳定磁和血铁矿石尾矿的弹性模量 (Mr).
- 确定影响这些材料的Mr的关键输入参数.
- 为传统实验室检测提供一个成本效益和时间效率高的替代方案.
主要方法:
- 收集了对水泥稳定磁铁矿石尾矿 (MIOT) 和血铁矿石尾矿 (HIOT) 的实验数据.
- 训练并比较各种ML模型,包括高斯过程回归 (GPR),以预测Mr.
- 进行了灵敏度分析,以确定输入变量的重要性:水泥含量,固化时间,批量应力和偏差应力.
主要成果:
- 高斯过程回归在预测MIOT和HIOT的Mr方面表现出卓越的准确性.
- 获得高的R平方值 (0.9936/0.9876对于MIOT列车/测试和0.9893/0.9825对于HIOT列车/测试).
- 敏感性分析表明,波特兰水泥的百分比是最重要的因素,而固化时间是最不重要的因素.
结论:
- ML模型,特别是GPR,提供了一种可靠和有效的方法来预测水泥稳定铁矿石尾矿的弹性模量.
- 这些发现有助于利用这些可持续材料优化路面设计.
- 了解输入参数的影响,可以更好地进行材料表征和质量控制.
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