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Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques

用基于树的 Bagging 和 Boosting 集成技术预测硬岩矿山岩爆

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Abstract

Rockburst prediction is of vital significance to the design and construction of underground hard rock mines. A rockburst database consisting of 102 case histories, i.e., 1998–2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods. The dataset was examined with six widely accepted indices which are: the maximum tangential stress around the excavation boundary (MTS), uniaxial compressive strength (UCS) and uniaxial tensile strength (UTS) of the intact rock, stress concentration factor (SCF), rock brittleness index (BI), and strain energy storage index (EEI). Two boosting (AdaBoost.M1, SAMME) and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated. The available dataset was randomly divided into training set (2/3 of whole datasets) and testing set (the remaining datasets). Repeated 10-fold cross validation (CV) was applied as the validation method for tuning the hyper-parameters. The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles. According to 10-fold CV, the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1, SAMME algorithms and empirical criteria methods.

摘要

岩爆预测对地下硬岩矿山的设计和施工至关重要. 使用三种基于树的集成方法, 对由 102 个历史案例(即1998—2011 年期间 14 个硬岩矿山数据)组成的岩爆数据库进行了检查, 以用于有岩爆倾向矿井的岩爆预测. 该岩爆数据集包含六个广泛接受的倾向性指标, 即: 开挖边界周围的最大切向应力(MTS)、 完整岩石的单轴抗压强度(UCS)和单轴抗拉强度(UTS)、 应力集中系数(SCF)、 岩石脆性指数(BI) 和应变能储存指数(EEI). 以分类树作为基准分类器的两种 Boosting 算法(AdaBoost.M1, SAMME)和 Bagging 算法进行了评估, 评估了它们学习岩爆的能力. 将可用数据集随机分为训练集(整个数据集的 2/3)和测试集(其余数据集). 采用重复 10 倍交叉验证(CV)作为调整模型超参数的验证方法, 并利用边际分析和变量相对重要性分析了各集成学习模型特征. 根据重复 10 倍交叉验证结果, 对岩爆数据集的精度分析表明, 与 AdaBoost.M1、 SAMME 算法和岩爆经验判据相比, Bagging 方法是预测硬岩矿山岩爆的最佳方法.

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Authors and Affiliations

  1. School of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Shi-ming Wang  (王世鸣)

  2. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Jian Zhou  (周健), Chuan-qi Li  (李传奇) & Xi-bing Li  (李夕兵)

  3. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Danial Jahed Armaghani

  4. Department of Mining and Materials Engineering, McGill University, Montreal, Canada

    Hani S. Mitri

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WANG Shi-ming and ZHOU Jian provided the concept and wrote the original draft of manuscript. ZHOU Jian, LI Chuan-qi and Danial Jahed ARMAGHANI collected, visualized and analyzed dataset, conducted the models. LI Xi-bing and Hani S. MITRI edited the draft of manuscript.

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Correspondence to Jian Zhou  (周健).

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Foundation item: Projects(41807259, 51604109) supported by the National Natural Science Foundation of China; Project(2020CX040) supported by the Innovation-Driven Project of Central South University, China; Project(2018JJ3693) supported by the Natural Science Foundation of Hunan Province, China

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Wang, Sm., Zhou, J., Li, Cq. et al. Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques. J. Cent. South Univ. 28, 527–542 (2021). https://doi.org/10.1007/s11771-021-4619-8

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