基于Python的回归机器学习模型的综合研究,用于使用Charnockite岩石中的多个参数来预测单轴压力强度
Sowmya Kochukrishnan1, Premalatha Krishnamurthy2, Yuvarajan D3
1Department of Civil Engineering, Anna University, Chennai, Tamil Nadu, India. sowmya.civil@gmail.com.
Scientific reports
|March 29, 2024
概括
机器学习模型使用间接因素,如超声波脉冲速度,准确地估计了Charnockite岩石中的单轴压缩强度 (UCS). 逐步回归模型展示了岩石强度的卓越预测准确性.
科学领域:
- 地质技术工程 地质技术工程
- 岩石机械学 岩石机械学
- 机器学习应用 机器学习应用
背景情况:
- 单轴压力 (UCS) 对地质机械应用至关重要.
- 对于某些岩石类型,直接UCS测试具有挑战性.
- 间接估计方法对于岩石的表征至关重要.
研究的目的:
- 开发和评估用于预测Charnockite UCS的机器学习模型.
- 为了比较简单线性回归和逐步回归模型的性能.
- 确定影响查诺基特UCS的关键因素.
主要方法:
- 在Python中实现简单的线性回归和逐步回归模型.
- 使用超声波脉冲速度 (UPV),施密特子反弹数 (N),巴西拉力强度 (BTS) 和点负载指数 (PLI) 作为输入参数.
- 使用回归系数 (R2),根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 的评估.
主要成果:
- 这两种模型都表现出对查尔诺基特UCS的高预测能力.
- 逐步回归模型实现了0.99 (测试) 和0.988 (培训) 的R2值.
- 超声波脉冲速度 (UPV) 被确定为UCS的重要预测因素.
结论:
- 机器学习模型提供了查诺基特UCS的准确间接估计.
- 循序渐进的回归是预测岩石强度的高效方法.
- UPV是评估岩石地质性能的一个关键参数.
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