A gas explosion in an underground structure may cause serious damage to the human body and ground buildings and may result in huge economic losses. The pressure of the gas explosion is an important parameter in determining its severity and designating an emergency plan. However, existing empirical and computational fluid dynamics (CFD) methods for pressure prediction are either inaccurate or inefficient when considering multiple influencing factors and their interrelationships. Therefore, for a more efficient and reliable prediction, the present study developed a multifactorial prediction model based on a beetle antennae search (BAS) algorithm improved back propagation (BP) neural network. A total of 317 sets of data which considered factors of geometry, gas, obstacle, vent, and ignition were collected from previous studies. The results showed that the established model can predict pressures accurately by low RMSE (43.4542 and 50.7176) and MAPE (3.9666% and 4.9605%) values and high R² (0.7696 and 0.7388) values for training and testing datasets, respectively. Meanwhile, the BAS algorithm was applied to improve both the calculation efficiency and the accuracy of the proposed model by enabling a more intelligent hyperparameter tuning method. Furthermore, the permutation importance of input variables was investigated, and the length (L) and the ratio of length and diameter (L/D) of geometry were found to be the most critical factors that affect the explosion pressure level.

地下结构中的瓦斯爆炸可能会对人体和地面建筑物造成严重损害，并可能导致巨大的经济损失。气体爆炸的压力是确定其严重性和制定应急计划的重要参数。但是，当考虑多个影响因素及其相互关系时，用于压力预测的现有经验和计算流体动力学（CFD）方法要么不准确，要么效率低下。因此，为了获得更有效和可靠的预测，本研究开发了一种基于甲虫天线搜索（BAS）算法和改进的反向传播（BP）神经网络的多因素预测模型。从以前的研究中收集了总共317套数据，这些数据考虑了几何形状，气体，障碍物，通风孔和着火的因素。分别用于训练和测试数据集的RMSE（43.4542和50.7176）和MAPE（3.9666％和4.9605％）值以及高R ²（0.7696和0.7388）值。同时，BAS算法通过启用更智能的超参数调整方法，从而提高了模型的计算效率和准确性。此外，研究了输入变量的排列重要性，发现几何形状的长度（L）和长度与直径之比（L / D）是影响爆炸压力水平的最关键因素。