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.

https://www.mdpi.com/2227-7390/8/5/765/pdf

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

Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods

Short-term rockburst risk prediction plays a crucial role in ensuring the safety of workers. However, it is a challenging task in deep rock engineering as it depends on many factors. More recently, machine learning approaches have started to be used to predict rockbursts. In this paper, ensemble learning methods including random forest (RF), adaptive boosting, gradient boosted decision tree (GBDT), extreme gradient boosting and light gradient boosting machine were adopted to predict short-term rockburst risk using microseismic data from the tunnels of Jinping-II hydropower project in China. First, labeled rockburst data with six indicators based on microseismic monitoring were collected. Then, the original rockburst data were randomly divided into training and test sets with a 70/30 sampling strategy. The hyperparameters of the ensemble learning methods were tuned with fivefold cross-validation during training. Finally, the predictive performance of each model was evaluated using classification accuracy, Cohen’s Kappa, precision, recall and F-measure metrics on the test set. The results showed that RF and GBDT possessed better overall performance. RF obtained the highest average accuracy of 0.8000 for all cases, whereas GBDT achieved the highest value for high (moderate and intense) risk cases with an accuracy of 0.9167. The proposed methodology can provide effective guidance for short-term rockburst risk management in deep underground projects.

Short-term rockburst risk prediction using ensemble learning methods

A two-step comprehensive evaluation model for rockburst prediction based on multiple empirical criteria

Mechanical properties and fracture characteristics of pre-holed rocks subjected to uniaxial loading: A comparative analysis of five hole shapes

Assessing the rockburst risk for deep shafts via distance-based multi-criteria decision making approaches with hesitant fuzzy information

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Assessing the rockburst risk for deep shafts via distance-based multi-criteria decision making approaches with hesitant fuzzy information