工矿自动化2026，Vol.52Issue(3)：123-132,10.DOI:10.13272/j.issn.1671-251x.2025070046
基于IBES-XGBoost的矿井巷道摩擦阻力因数预测模型
Prediction model for friction resistance coefficient of mine roadways based on IBES-XGBoost
摘要
Abstract
To address the issues of underfitting or overfitting and low prediction accuracy in existing machine learning-based algorithms for predicting the friction resistance coefficient α of mine roadways,an Improved Bald Eagle Search(IBES)algorithm was proposed.This algorithm integrated reverse learning initialization,chaotic adaptive parameters,dynamic adaptive mutation,and chaotic local search strategies.It was employed to adaptively optimize the key hyperparameters of the Extreme Gradient Boosting(XGBoost)model.On this basis,a prediction model for the roadway friction resistance coefficient α(IBES-XGBoost model)was constructed,using multi-dimensional roadway geometric parameters and structural category information as input features,and minimizing the Root Mean Square Error(RMSE)as the objective function.Standard benchmark function tests demonstrated that the IBES algorithm exhibited significant advantages over the original Bald Eagle Search algorithm and other metaheuristic algorithms in terms of solution accuracy,convergence speed,and stability.A dataset comprising 260 samples was established based on field measurements from multiple mines in northern Shaanxi,covering complex working conditions including four typical cross-sectional shapes and eight support types.Stratified sampling based on support types was performed,and the dataset was divided into training and test sets at a ratio of 8∶2.Hyperparameter optimization was completed using five-fold cross-validation.Experimental results showed that the IBES-XGBoost model achieved an RMSE of 0.001 232,a mean absolute error(MAE)of 0.000 868,and a coefficient of determination(R2)of 0.985 426 on the test set,outperforming all comparison models.Compared with the second-best BES-XGBoost model,the RMSE and MAE were reduced by 49.94％and 49.09％,respectively,demonstrating that the IBES-XGBoost model possessed extremely high prediction accuracy and robustness.关键词
矿井通风/巷道摩擦阻力因数/极限梯度提升树/改进秃鹰搜索算法/超参数寻优/预测模型Key words
mine ventilation/roadway friction resistance coefficient/Extreme Gradient Boosting/Improved Bald Eagle Search algorithm/hyperparameter optimization/prediction model分类
矿业与冶金引用本文复制引用
闫振国,周勃兴,王延平,秦志鑫,张嘉珞,何世龙,袁林雨..基于IBES-XGBoost的矿井巷道摩擦阻力因数预测模型[J].工矿自动化,2026,52(3):123-132,10.基金项目
国家自然科学基金项目(52074214) （52074214）
陕西省重点研发计划项目(2023-YBSF-190). （2023-YBSF-190）
