混合堆叠集合算法和模拟退火优化用于地下进入式挖掘的稳定性评估

IF 8.2 1区 工程技术 Q1 ENGINEERING, CIVIL
Leilei Liu, Guoyan Zhao, Weizhang Liang, Zheng Jian
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引用次数: 0

摘要

地下巷道开挖(UETE)的稳定性对于确保采矿作业的安全至关重要。随着更多工程案例的积累,机器学习(ML)在 UETE 的稳定性评估方面展现出巨大潜力。本研究提出了一种混合堆叠集合方法,将支持向量机 (SVM)、k-近邻 (KNN)、决策树 (DT)、随机森林 (RF)、多层感知器神经网络 (MLPNN) 和极梯度提升 (XGBoost) 算法集合在一起,用于评估 UETE 的稳定性。首先,从 7 个矿井中收集了共 399 个历史案例,包含两个指标。随后,为了追求更好的评估性能,结合五倍交叉验证(CV)和模拟退火(SA)方法,对基础学习器(SVM、KNN、DT、RF、MLPNN 和 XGBoost)和元学习器(MLPNN)的超参数进行了调整。根据最优超参数配置,使用训练集(75% 的数据)构建了堆叠集合模型。最后,通过测试集(25% 的数据)上的两个全局指标(准确率和 Cohen's Kappa)和三个类内指标(精确度、召回率和 F1 分数的宏观平均值)评估了所提方法的性能。此外,还将评估结果与通过 SA 优化的六个基础学习器进行了比较。混合堆叠集合算法的准确度、卡帕系数、精确度、召回率和 F1 分数的宏观平均值分别为 0.92、0.851、0.885、0.88 和 0.883,取得了较好的综合性能。岩石质量等级(RMR)对评价结果的影响最大。此外,根据提出的模型更新了临界跨度图(CSG),与之前的研究相比有了显著改善。这项研究可为 UETE 的稳定性分析和风险管理提供有价值的指导。不过,今后有必要考虑更多指标,收集更广泛、更均衡的数据集来验证模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid stacking ensemble algorithm and simulated annealing optimization for stability evaluation of underground entry-type excavations

The stability of underground entry-type excavations (UETEs) is of paramount importance for ensuring the safety of mining operations. As more engineering cases are accumulated, machine learning (ML) has demonstrated great potential for the stability evaluation of UETEs. In this study, a hybrid stacking ensemble method aggregating support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), multilayer perceptron neural network (MLPNN) and extreme gradient boosting (XGBoost) algorithms was proposed to assess the stability of UETEs. Firstly, a total of 399 historical cases with two indicators were collected from seven mines. Subsequently, to pursue better evaluation performance, the hyperparameters of base learners (SVM, KNN, DT, RF, MLPNN and XGBoost) and meta learner (MLPNN) were tuned by combining a five-fold cross validation (CV) and simulated annealing (SA) approach. Based on the optimal hyperparameters configuration, the stacking ensemble models were constructed using the training set (75% of the data). Finally, the performance of the proposed approach was evaluated by two global metrics (accuracy and Cohen’s Kappa) and three within-class metrics (macro average of the precision, recall and F1-score) on the test set (25% of the data). In addition, the evaluation results were compared with six base learners optimized by SA. The hybrid stacking ensemble algorithm achieved better comprehensive performance with the accuracy, Kappa coefficient, macro average of the precision, recall and F1-score were 0.92, 0.851, 0.885, 0.88 and 0.883, respectively. The rock mass rating (RMR) had the most important influence on evaluation results. Moreover, the critical span graph (CSG) was updated based on the proposed model, representing a significant improvement compared with the previous studies. This study can provide valuable guidance for stability analysis and risk management of UETEs. However, it is necessary to consider more indicators and collect more extensive and balanced dataset to validate the model in future.

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来源期刊
Underground Space
Underground Space ENGINEERING, CIVIL-
CiteScore
10.20
自引率
14.10%
发文量
71
审稿时长
63 days
期刊介绍: Underground Space is an open access international journal without article processing charges (APC) committed to serving as a scientific forum for researchers and practitioners in the field of underground engineering. The journal welcomes manuscripts that deal with original theories, methods, technologies, and important applications throughout the life-cycle of underground projects, including planning, design, operation and maintenance, disaster prevention, and demolition. The journal is particularly interested in manuscripts related to the latest development of smart underground engineering from the perspectives of resilience, resources saving, environmental friendliness, humanity, and artificial intelligence. The manuscripts are expected to have significant innovation and potential impact in the field of underground engineering, and should have clear association with or application in underground projects.
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