Yuan Haiping, Ji Shuaijie, Zhu Chuanqi, Wang Lei
State Key Laboratory of Mining Induced Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China.
College of Civil Engineering, Hefei University of Technology, Hefei 230009, China.
Biomimetics (Basel). 2024 Jun 28;9(7):394. doi: 10.3390/biomimetics9070394.
In general, the design of a safe and rational laneway support scheme signifies a crucial prerequisite for ensuring the security and efficiency of mining exploitation in mines. Nevertheless, the conventional empirical support system for mining laneways faces challenges in assessing the rationality of support methods, which can compromise the safety and reliability of the laneways. To address this issue, the safety factor was incorporated into research on laneway support, and a safety evaluation method for laneway support in line with the safety factor was established. In light of the data from a specific iron mine laneway in central China, the CRITIC method was employed to preprocess the sample data. Going one step further, a Bayesian algorithm was utilized to optimize the hyperparameters of the CatBoost model, followed by proposing a prediction model based on the BO-CatBoost model for evaluating laneway safety factors of plain shotcrete support. Furthermore, the performance indexes, such as the root mean square error (), the mean absolute error (), the correlation coefficient (), the variance accounts for (), and the a-20 index, were determined to examine the predictive performance of each proposed model. In contrast to the other models, the BO-CatBoost model demonstrated the optimal predictive output item for safety factors with the lowest and , the largest and , and an appropriate a-20 index value of 0.5688, 0.4074, 0.9553, 95.25%, and 0.9167 in the test set, respectively. Therefore, the BO-CatBoost model was proven to be the most appropriate machine learning method that can more accurately predict the safety factor, which will provide a novel approach for optimizing laneway support design and laneway safety evaluation.
一般来说，设计安全合理的巷道支护方案是确保矿山开采安全与效率的关键前提。然而，传统的矿山巷道经验支护体系在评估支护方法的合理性方面面临挑战，这可能会影响巷道的安全性和可靠性。为解决这一问题，将安全系数纳入巷道支护研究，并建立了符合安全系数的巷道支护安全评价方法。根据中国中部某特定铁矿巷道的数据，采用CRITIC方法对样本数据进行预处理。进一步利用贝叶斯算法优化CatBoost模型的超参数，随后提出基于BO - CatBoost模型的预测模型，用于评估素喷混凝土支护巷道的安全系数。此外，还确定了均方根误差（）、平均绝对误差（）、相关系数（）、方差贡献率（）和a - 20指数等性能指标，以检验各模型的预测性能。与其他模型相比，BO - CatBoost模型在测试集中展示了安全系数的最优预测输出项，其均方根误差和平均绝对误差最低，相关系数和方差贡献率最大，a - 20指数值分别为0.5688、0.4074、0.9553、95.25%和0.9167。因此，BO - CatBoost模型被证明是最适合的机器学习方法，能够更准确地预测安全系数，这将为优化巷道支护设计和巷道安全评价提供一种新方法。
