The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel.

中文翻译：

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基于深度神经网络的岩爆预测与评价

岩爆的形成机制复杂，其预测一直是工程难题。根据隧道工程数据，建立三维离散元数值模型，分析隧道初始应力特性。建立了岩爆预测的神经网络模型。选择单轴抗压强度、单轴抗拉强度、最大主应力和岩石弹性能作为岩爆预测的输入参数。通过现有数据进行训练。神经网络模型表明，岩爆风险与最大主应力密切相关。在划分岩爆危险区的基础上，根据不同的岩爆等级提出相应的治理方法，避免岩爆灾害的发生。