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开发先进的数据驱动框架来预测岩石上堆的承载能力
Kennedy C Onyelowe1,2, Shadi Hanandeh3, Viroon Kamchoom4
1Department of Civil Engineering, College of Eng & Eng Technology, Michael Okpara University of Agriculture, Umudike, Nigeria. kennedychibuzor@kiu.ac.ug.
这项研究引入了一种机器学习框架,以准确预测岩石上的堆承载能力. 透阻力和嵌入深度等关键因素显著影响预测,提高了基础设计.
科学领域:
- 地质技术工程 地质技术工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 准确预测岩石上的承载能力对于基础稳定至关重要.
- 对于复杂的地质条件,传统方法往往缺乏精度.
研究的目的:
- 开发一个先进的数据驱动框架,用于预测岩石上的堆承载能力.
- 为此目的集成和评估多个机器学习算法.
主要方法:
- 使用了一套全面的数据集,包括堆尺寸,地质特征和现场测试参数.
- 经过训练和验证的模型包括Kstar,M5Rules,ElasticNet,XNV和决策树.
- 使用泰勒图,统计评估和SHAP值进行分析.
主要成果:
- 提出的机器学习模型在捕捉非线性关系方面表现出卓越的表现.
- 高相关系数和低平方根平均误差证实了强大的预测能力.
- 透阻力,嵌深度和地质条件被确定为关键影响参数.
结论:
- 机器学习为堆容量预测的传统方法提供了可靠和高效的替代方案.
- 开发的框架提高了堆设计的准确性,并减少了地质工程中的不确定性.
- 进一步的研究应该探索不同的地质条件和混合建模技术.
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