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

• Published in: Mathematics (Basel) Vol. 8; no. 5; p. 765
• Main Authors: Liang, Weizhang, Luo, Suizhi, Zhao, Guoyan, Wu, Hao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2020
• Subjects: Algorithms; Decision trees; Discrete element method; extreme gradient boosting (XGBoost); gradient boosting decision tree (GBDT); hard rock; Indicators; light gradient boosting machine (LightGBM); Machine learning; Mathematical models; Mathematics; Methods; Mine safety; Mines; Neural networks; Performance evaluation; Performance indices; Performance prediction; pillar stability; prediction; Prediction models; Risk management; Safety factors; Sensitivity analysis; Simulation; Stability; Support vector machines; United States
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math8050765

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 F1 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.