基于PSO-XGBoost混合模型预测岩石卸载强度
Baohua Liu1, Hang Lin1, Yifan Chen1
1School of Resources and Safety Engineering, Central South University, Changsha 410083, China.
Materials (Basel, Switzerland)
|September 14, 2024
概括
这项研究引入了一种混合学习模型,用于预测岩石卸载强度,其性能优于其他方法. PSO-XGBoost模型为岩石挖掘工程提供了一种可靠的方法.
科学领域:
- 地质技术工程 地质技术工程
- 计算力学 计算力学 计算力学
- 在工程领域的人工智能.
背景情况:
- 岩石挖掘涉及与加载相比具有独特机械性能的卸载行为.
- 在卸载条件下准确预测岩石的峰值强度仍然是一个挑战.
- 现有的模型往往缺乏准确性来预测岩石卸载强度.
研究的目的:
- 开发一种智能混合学习模型,用于预测岩石卸载强度.
- 利用来自岩石卸载测试的简单参数进行准确的预测.
- 为了提高预测准确度,超越传统的机器学习模型.
主要方法:
- 开发了一个混合XGBoost模型,使用粒子群优化 (PSO) 来进行参数调整.
- 从全球出版物中收集了来自不同类型岩石的134个岩石样本数据集.
- 性能与随机森林 (RF),支持矢量机 (SVM),XGBoost和网格搜索XGBoost (GS-XGBoost) 模型进行了评估.
- 统计指标 (R2,MAE,MAPE,MSE,RMSE) 用于进行定量评估.
主要成果:
- 拟议的PSO-XGBoost混合模型在预测岩石卸载强度方面表现出卓越的性能.
- 定量分析证实了PSO-XGBoost模型的提高准确性和可靠性.
- 特性重要性分析提供了对输入参数对模型概括的影响的见解.
结论:
- PSO-XGBoost模型在预测岩石卸载强度方面取得了重大进展.
- 该模型的有效性为道挖掘设计和相关的地质工程项目提供了宝贵的参考.
- 智能混合学习模型显示出解决复杂的地质工程挑战的巨大潜力.
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