Shock wave pressure, high temperature flames, and toxic gases are among the factors that cause mine ventilation system failure following a gas explosion. Herein, we propose a Support Vector Regression (SVR)-based machine learning model to quickly determine the propagation of these disaster-causing hazards throughout the whole ventilation network. FLACS was used to simulate the explosions in a straight roadway with different spatial geometric parameters and gas explosion equivalents. Four SVR machine learning models were constructed by incorporating the roadway’s cross-sectional area, length of gas filling, gas concentration, and the distance between the observation point and the explosion source as inputs, while the shock wave pressure, flame temperature, time to the maximum temperature, and pressure served as outputs. The proposed model can quickly and accurately predict the propagation of disaster-causing hazards for a given explosion position and equivalent. As such, it plays a significant role in determining the ventilation system failure mode and aids decision makers and rescuers in developing a rescue and refuge plan.

冲击波压力，高温火焰和有毒气体是导致瓦斯爆炸后矿井通风系统故障的因素。在本文中，我们提出了一种基于支持向量回归（SVR）的机器学习模型，以快速确定这些导致灾难的危害在整个通风网络中的传播。FLACS用于模拟具有不同空间几何参数和气体爆炸当量的直巷中的爆炸。通过将巷道的横截面积，加气长度，瓦斯浓度和观察点与爆炸源之间的距离作为输入，并结合冲击波压力，火焰温度，到达隧道的时间，构建了四个SVR机器学习模型。最高温度和压力作为输出。所提出的模型可以快速准确地预测给定爆炸位置和当量爆炸的致灾危险的传播。因此，它在确定通风系统故障模式方面起着重要作用，并有助于决策者和营救者制定营救计划。