The prediction of the risk of rockbursts in burst-prone grounds is turned into a challenging and vital mission for most underground projects that attract great interest from engineers and researchers. In this study, a hybrid technique, the artificial neural network (ANN) and artificial bee colony (ABC), neuro-bee model, was considered to create the sophisticated relationship between the risk of rockbursts in burst-prone grounds and its influencing factors. The establishment and validation of ANN models were implemented via a data set extracted from previous works, and the database covers 246 reliable rockburst cases. Six influencing factors were selected as input variables. Five-fold cross validation were adopted to tune hyper-parameters of ABC-ANN models, and the performance of ANN models was evaluated by correlation coefficient (R²) and root mean square error (RMSE). Observational experiment results indicated that the ABC-ANN algorithm can be utilized as an effective tool for predicting the risk of rockbursts in burst-prone grounds. The R² and RMSE values between the predicted and actual rockburst values were 0.9656 and 0.1281, respectively. Sensitivity analyses implemented by the response surface method revealed that the maximum tangential stress of the cavern wall and the elastic strain index parameters have a greater effects on rockburst compared with other input parameters. As a result, the proposed hybrid method outperforms the other models for rockburst prediction in terms of the prediction accuracy and the generalization capability.

对于易爆地面中的岩爆风险的预测，对大多数吸引工程师和研究人员极大兴趣的地下项目而言，已变成一项具有挑战性且至关重要的任务。在这项研究中，一种混合​​技术，即人工神经网络（ANN）和人工蜂群（ABC），神经蜂模型，被认为可以在易爆场地中的岩爆风险与其影响因素之间建立复杂的关系。ANN模型的建立和验证是通过从先前工作中提取的数据集实现的，该数据库涵盖了246个可靠的岩爆案例。选择六个影响因素作为输入变量。采用五重交叉验证对ABC-ANN模型的超参数进行微调，并通过相关系数评估ANN模型的性能（R ²）和均方根误差（RMSE）。观测实验结果表明，ABC-ANN算法可作为预测易发爆裂场地中岩爆危险的有效工具。的- [R ²的预测值和实际值的岩爆之间和RMSE值分别为0.9656和0.1281。通过响应面法进行的敏感性分析表明，与其他输入参数相比，洞壁的最大切向应力和弹性应变指数参数对岩爆的影响更大。结果，所提出的混合方法在预测精度和泛化能力方面都优于其他岩爆预测模型。