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轻GBM集成与修改的数据平衡和鱼优化算法用于岩石质量分类.

Long Li1

  • 1School of Management Science and Engineering, Shandong Technology and Business University, Yantai, 264005, Shandong, China.

Scientific reports
|October 3, 2024
PubMed
概括
此摘要是机器生成的。

使用改进的EWOA和LightGBM模型进行精确的岩石质量分类,提高了道钻机操作. 这种方法提高了对具有挑战性的岩石类的预测准确度,提高了TBM道效率.

关键词:
数据平衡算法数据平衡算法改进了鱼优化算法.光梯度增强机器可以提高光梯度.岩石质量分类的分类

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科学领域:

  • 地质技术工程 地质技术工程
  • 在土木工程中的人工智能.
  • 岩石机械学 岩石机械学

背景情况:

  • 精确的岩石质量分类对于智能道钻机 (TBM) 操作至关重要.
  • 地质变化和复杂性对传统的岩石质量分类方法构成重大挑战.
  • 现有的方法往往与少数岩石质量类别发生冲突,影响整体预测的准确性.

研究的目的:

  • 开发一种创新的预测模型,用于在TBM道挖掘中准确地分类岩石质量.
  • 提高预测准确度,特别是对于少数岩石质量类 (II,IV,V).
  • 通过改善地质理解,提高TBM施工的效率和智能.

主要方法:

  • 集成一个改进的皇帝企优化算法 (IEWOA) 与一个新的参数"l"和正弦函数,以优化搜索.
  • 应用少数阶级技术,增强随机步行策略 (MCT-RW) 以扩大少数阶级边界.
  • 在分类任务中使用光梯度增强机 (LightGBM).

主要成果:

  • 拟议的IEWOA-LightGBM模型在岩石质量分类中实现了94.74%的卓越准确性.
  • 在少数岩石质量类别中观察到回忆和F1得分的显著改善.
  • 该MCT-RW战略有效地扩大了具有挑战性的岩石类别的边界.

结论:

  • IEWOA-LightGBM模型为TBM道中的岩石质量分类提供了强大的和准确的解决方案.
  • 这种方法显著提高了TBM建设的智能操作和效率.
  • 该研究为解决地下工程的关键挑战做出了有价值的贡献.