基于基因表达编程和粒子群优化鲁棒混合计算模型的深部地下矿井岩爆分类
作者:
作者单位:

1.Department of Surface Mining, Mining Faculty;2.Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, Hanoi 100000, Vietnam

作者简介:

通讯作者:

中图分类号:

基金项目:


Classifying rockburst in deep underground mines using a robust hybrid computational model based on gene expression programming and particle swarm optimization
Author:
Affiliation:

1.Department of Surface Mining, Mining Faculty;2.Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, Hanoi 100000, Vietnam

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    在深部地下采矿中,岩爆因具有许多不利影响(如,对人员、设备、隧道/地下矿山工作面和开采周期等的影响)而被视为不确定性风险。由于其不确定性的特征,对岩爆趋势的准确预测和分类具有一定难度,且已有研究成果较少。提出一种基于基因表达编程(GEP)和粒子群优化(PSO)的鲁棒混合计算模型GEP-PSO,用于预测和分类深部开口的岩爆趋势,提高了预测和分类的准确性。在建立GEP-PSO模型过程中,评估了GEP模型中不同数量的基因(1~4)和连接功能(例如,加法、提取、乘法和除法)。收集了246次岩爆发生的地质和施工因素,用于建立岩爆分类的GEP-PSO模型;应用处理数据集缺失值的技术改进数据集的属性;用相关矩阵选取潜在输入参数的特征;建立了13个混合GEP-PSO模型,得到了各模型的精度。结果表明:在GEP结构中具有3个基因和乘法连接函数的GEP-PSO模型具有最高的准确度(80.49%)。将获得的最佳GEP-PSO模型的结果与基于相同数据集开发的各种已有模型进行比较,结果表明:选择的GEP-PSO模型结果优于已有模型,表明提出的GEP-PSO模型在岩爆等级的预测和分类方面的准确性显著提高,可以应用于深开挖工程中,以准确预测和评估岩爆敏感性。

    Abstract:

    In deep underground mining, rockburst is taken into account as an uncertainty risk with many adverse effects (i.e., human, equipment, tunnel/underground mine face, and extraction periods). Due to its uncertainty characteristics, accurate prediction and classification of rockburst tendency are challenging, and previous results are poor. Therefore, this study proposed a robust hybrid computational model based on gene expression programming (GEP) and particle swarm optimization (PSO), called GEP-PSO, to predict and classify rockburst tendency in deep openings with an accuracy improved. A different number of genes (from 1 to 4) and linking functions (e.g., addition, extraction, multiplication, and division) in the GEP model were also evaluated during the development of the GEP-PSO model aim. Geotechnical and constructive factors of 246 rockburst events were collected and used to develop the GEP-PSO models in terms of rockburst classification. Subsequently, a robust technique to handle missing values of the dataset was applied to improve the dataset""s attributes. The last step in the data processing stage is the feature selection to select potential input parameters using a correlation matrix. Finally, 13 hybrid GEP-PSO models were developed with different accuracies reported. The findings indicated that the GEP-PSO model with three genes in the structure of GEP and the multiplication linking function provided the highest accuracy (i.e., 80.49%). The obtained results of the best GEP-PSO model were then compared with a variety of previous models developed by previous researchers based on the same dataset. The comparison results also showed that the selected GEP-PSO model results outperform those of previous models. In other words, the accuracy of the proposed GEP-PSO model was improved significantly in terms of prediction and classification of rockburst grade. It can be considered widely applied in deep openings aiming to predict and evaluate the rockburst susceptibility accurately.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2022-09-07
  • 最后修改日期:2022-09-07
  • 录用日期:
  • 在线发布日期: 2022-09-07
  • 出版日期: