The time-dependent deformation of rocks due to stress released by excavation is referred to as squeezing. Accurate evaluation of the squeezing at the design stage can dramatically reduce technical problems and the financial costs of underground structures. Although various methods are presented to predict tunnel squeezing at the preliminary stage, being site-specific and incorporating incomplete databases are deficiencies of the available procedures. In this study, based on a comprehensive literature review, we prepared a database of tunnel squeezing for soft rocks, including possible effective parameters. Statistical processing methods such as univariate, reduction, and cleaning were employed to improve the statistical quality of the database. The statistically-processed datasets were also validated based on various scales such as accuracy, convergence, and usefulness. Significant predictors of squeezing are recognized as the ratio of strength to stress and the rock mass classification system. New squeezing criteria were developed using binary and multi-class regression methods to predict the squeezing occurrence and intensity of soft rocks. The results are confirmed by a Multilayer Perceptron Feed-Forward Neural Network and are compared to well-known empirical equations. The developed equations are more accurate comparing the empirical equations used to predict the squeezing of soft rocks. This methodology can be utilized at the design stage for another database to predict squeezing rocks for topographic-stress and tectonic-stress-based cases.

由开挖释放的应力引起的随时间变化的岩石变形称为挤压。在设计阶段准确评估挤压可以显着减少技术问题和地下结构的财务成本。尽管提出了各种方法来预测初步阶段的隧道挤压，但特定于站点且包含不完整的数据库是可用程序的不足之处。在这项研究中，在综合文献回顾的基础上，我们准备了一个软岩隧道挤压数据库，包括可能的有效参数。采用单变量、归约、清洗等统计处理方法提高数据库的统计质量。统计处理的数据集也根据各种尺度进行了验证，例如准确性，收敛性和实用性。挤压的重要预测因素被认为是强度与应力的比率和岩体分类系统。使用二元和多类回归方法开发了新的挤压准则来预测软岩的挤压发生和强度。结果由多层感知器前馈神经网络证实，并与众所周知的经验方程进行比较。与用于预测软岩挤压的经验方程相比，所开发的方程更准确。这种方法可以在另一个数据库的设计阶段使用，以预测基于地形应力和构造应力的情况下的挤压岩。挤压的重要预测因素被认为是强度与应力的比率和岩体分类系统。使用二元和多类回归方法开发了新的挤压准则来预测软岩的挤压发生和强度。结果由多层感知器前馈神经网络证实，并与众所周知的经验方程进行比较。与用于预测软岩挤压的经验方程相比，所开发的方程更准确。这种方法可以在另一个数据库的设计阶段使用，以预测基于地形应力和构造应力的情况下的挤压岩。挤压的重要预测因素被认为是强度与应力的比率和岩体分类系统。使用二元和多类回归方法开发了新的挤压准则来预测软岩的挤压发生和强度。结果由多层感知器前馈神经网络证实，并与众所周知的经验方程进行比较。与用于预测软岩挤压的经验方程相比，所开发的方程更准确。这种方法可以在另一个数据库的设计阶段使用，以预测基于地形应力和构造应力的情况下的挤压岩。结果由多层感知器前馈神经网络证实，并与众所周知的经验方程进行比较。与用于预测软岩挤压的经验方程相比，所开发的方程更准确。这种方法可以在另一个数据库的设计阶段使用，以预测基于地形应力和构造应力的情况下的挤压岩。结果由多层感知器前馈神经网络证实，并与众所周知的经验方程进行比较。与用于预测软岩挤压的经验方程相比，所开发的方程更准确。这种方法可以在另一个数据库的设计阶段使用，以预测基于地形应力和构造应力的情况下的挤压岩。