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

TRAN Quang-Hieu; BUI Xuan-Nam; NGUYEN Hoang

doi:10.11835/j.issn.2096-6717.2022.023

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

MLDSG3 | 更新时间：2023-03-07

- Classifying rockburst in deep underground mines using a robust hybrid computational model based on gene expression programming and particle swarm optimization
- Journal of Civil and Environmental Engineering Vol. 45, Issue 2, Pages: 21-38(2023)
- 作者机构：
  
  Department of Surface Mining, Mining Faculty
  Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, Hanoi 100000, Vietnam
- 作者简介：
  
  TRAN Quang-Hieu, PhD, main research interests: rock mechanics; blasting, occupational safety and health in mining, E-mail: tranquanghieu@humg.edu.vn.
- 基金信息：
- DOI：10.11835/j.issn.2096-6717.2022.023
  CLC： TU457;TD311
- Received：16 November 2021，
  
  Published：25 April 2023
- 稿件说明：
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TRAN Quang-Hieu, BUI Xuan-Nam, NGUYEN Hoang. Classifying rockburst in deep underground mines using a robust hybrid computational model based on gene expression programming and particle swarm optimization[J]. Journal of Civil and Environmental Engineering, 2023, 45(2): 21-38.
DOI：

TRAN Quang-Hieu, BUI Xuan-Nam, NGUYEN Hoang. Classifying rockburst in deep underground mines using a robust hybrid computational model based on gene expression programming and particle swarm optimization[J]. Journal of Civil and Environmental Engineering, 2023, 45(2): 21-38. DOI： 10.11835/j.issn.2096-6717.2022.023.

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模型的过程中，评估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13个混合GEP-PSO模型，得到各模型的精度。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 susceptibility are challenging

and previous results are limited. 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 potential in deep openings with improved accuracy. 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 for development of the GEP-PSO models. 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 determine potential input parameters using a correlation matrix. Finally

13 hybrid GEP-PSO models were developed with varying accuracies. 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 susceptibility. It can be considered widely applied in deep openings aiming to predict and evaluate the rockburst susceptibility accurately.

关键词

Keywords

references

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