一个机器学习框架,用于预测岩石材料的剪强性能
Daxing Lei1,2, Yaoping Zhang3,4, Zhigang Lu3,4
1School of Resources and Civil Engineering, Gannan University of Science and Technology, Ganzhou, 341000, China. daxinglei17@163.com.
Scientific reports
|March 14, 2025
概括
这项研究引入了一个新的机器学习模型,ISSA-XGBoost,用于预测岩石剪切强度. 该模型准确地估计了凝聚力和内部摩擦角度,改善了岩石工程设计.
科学领域:
- 地质技术工程 地质技术工程
- 计算智能是一种计算智能.
背景情况:
- 岩石剪切强度参数 (凝聚力和内部摩擦角度) 对于设计稳定的岩石结构至关重要.
- 准确预测这些参数可以减少工程时间,成本和物理测试的材料浪费.
研究的目的:
- 开发一种新的机器学习模型,用于预测岩石剪切强度特征.
- 提高地质工程中的岩石强度预测的准确性和效率.
主要方法:
- 利用了199个喜马拉雅岩石样本的数据集,并使用了6个输入变量.
- 开发并实施了改进的搜索算法 (ISSA) 与极端梯度提升 (XGBoost) 结合 - - ISSA-XGBoost模型.
- 与其他模型对比ISSA-XGBoost并进行特征重要性分析.
主要成果:
- 该ISSA-XGBoost模型显示出卓越的预测准确性,实现R2 = 0.982的凝聚力和R2 = 0.932的内部摩擦角度.
- 单轴压力强度和P波速度被确定为凝聚力的关键预测因素.
- 密度和P波速度被发现是对内部摩擦角度最有影响的因素.
结论:
- ISSA-XGBoost模型提供了一个非常准确和高效的方法来预测岩石剪切强度.
- 这些发现为影响岩石机械性能的关键因素提供了宝贵的见解,有助于更可靠的地质技术设计.
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