【摘要】 矿柱是保障地下矿山开采安全的重要支撑结构，准确预测矿柱稳定性对地下空间安全至关重要。为此，提出一种新颖的稳健随机森林算法预测矿柱稳定性。首先，建立了包含317组样本的硬岩矿柱稳定性数据集，采用交叉递归特征消除方法选取矿柱宽度、高度、宽高比、单轴抗压强度、平均承载应力以及平均承载应力与矿岩单轴抗压强度之比共6个特征作为输入参数，并借助链式方程多重插补和孤立随机森林算法分别填充和剔除了数据集中的缺失值和异常值；其次，针对传统随机森林算法中低质量决策树可能引起的决策冗余和性能损失问题，引入决策树纯化干预机制和精确率加权优化策略提升随机森林算法的决策效率和预测精度，进而构建基于稳健随机森林的硬岩矿柱稳定性预测模型；最后，从性能评估、模型比较、模型解释和工程验证4个方面检验了模型的准确性、先进性和可靠性。结果表明，所提算法无需参数优化即可取得较好的预测性能。模型预测准确率为88.9%，预测性能优于其他机器学习模型，可为地下矿山硬岩矿柱的稳定性评估提供有效指导。更多还原

【Abstract】 Rock pillars are critical support structures that ensure safe mining operations in underground mines. Accurately predicting rock pillar stability is essential for ensuring safety in underground spaces. Consequently, we propose a novel robust random forest algorithm to predict rock pillar stability. First, we established a dataset comprising 317 hard rock pillars. We select six feature variables as input parameters through recursive feature elimination with cross-validation: pillar width, pillar height, the ratio of pillar width to height, uniaxial compressive strength, average pillar stress, and the ratio of average pillar stress to uniaxial compressive strength. Additionally, we employ chained equation multiple imputation and isolated random forest algorithms to address missing values and outliers. Second, to address decision redundancy and performance loss potentially caused by low-quality decision trees in the random forest algorithm, we introduce a decision tree purification mechanism and a precision weighting strategy. This enhances decision-making efficiency and prediction accuracy, leading to the development of a robust random forest model for predicting hard rock pillar stability. Finally, we evaluate the accuracy, advancement, and reliability of the models through performance evaluation, model comparison, model explanation, and engineering validation. The results indicate that the proposed algorithm achieves satisfactory predictive performance without requiring parameter optimization. The model’s predictive accuracy reaches 88.9%, outperforming other machine learning models, thereby providing effective guidance for evaluating the stability of hard rock pillars in underground mines.更多还原