Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

Weizhang Liang, Suizhi Luo, Guoyan Zhao and Hao Wu
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Weizhang Liang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Suizhi Luo: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Guoyan Zhao: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Hao Wu: School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Mathematics, 2020, vol. 8, issue 5, 1-17

Abstract: Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great potential to predict pillar stability. This study aims to predict hard rock pillar stability using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms. First, 236 cases with five indicators were collected from seven hard rock mines. Afterwards, the hyperparameters of each model were tuned using a five-fold cross validation (CV) approach. Based on the optimal hyperparameters configuration, prediction models were constructed using training set (70% of the data). Finally, the test set (30% of the data) was adopted to evaluate the performance of each model. The precision, recall, and F 1 indexes were utilized to analyze prediction results of each level, and the accuracy and their macro average values were used to assess the overall prediction performance. Based on the sensitivity analysis of indicators, the relative importance of each indicator was obtained. In addition, the safety factor approach and other ML algorithms were adopted as comparisons. The results showed that GBDT, XGBoost, and LightGBM algorithms achieved a better comprehensive performance, and their prediction accuracies were 0.8310, 0.8310, and 0.8169, respectively. The average pillar stress and ratio of pillar width to pillar height had the most important influences on prediction results. The proposed methodology can provide a reliable reference for pillar design and stability risk management.

Keywords: pillar stability; hard rock; prediction; gradient boosting decision tree (GBDT); extreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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