Tunnel face is important for shallow tunnels to avoid collapses. In this study, tunnel face stability is studied with soft computing techniques. A database is created based on the literature which is used to train some broadly adopted soft computing techniques, ranging from linear regression to the artificial neural network. The soil dry density, cohesion, friction angle, cover depth and the tunnel diameter are used as the input parameters. The soft computing techniques state whether the face support is stable and predict the face support pressure. It is found that the artificial neural network outperforms the other techniques. The face support pressure is predicted with the artificial neural network for statistically distributed samples, and the failure probability is obtained with Monte Carlo simulations. In this way, the stability of the tunnel face can be reliably assessed and the support pressure can be estimated fairly accurately.

隧道掌子面对于浅层隧道避免坍塌很重要。在本研究中，采用软计算技术研究了掌子面稳定性。数据库是基于文献创建的，用于训练一些广泛采用的软计算技术，从线性回归到人工神经网络。土壤干密度、黏聚力、摩擦角、覆盖层深度和隧道直径用作输​​入参数。软计算技术说明面部支撑是否稳定并预测面部支撑压力。发现人工神经网络优于其他技术。对于统计分布的样本，采用人工神经网络预测工作面支撑压力，并通过蒙特卡罗模拟获得失效概率。这样，