LAUSR

This paper presents the analysis and intelligent prediction for the displacement of stratum and tunnel lining of Qingdao Metro Line 4 by earth pressure balance (EPB) shield tunnel excavation in complex strata. When the tunnel is excavated in different stratum sections, the tunneling parameters of shield machine are systematically analyzed and compared, and the vertical displacement of the tunnel crown and the horizontal convergence deformation on both sides are investigated. When the tunnel body passes through the soft soil stratum and rock stratum, the curves of the vertical displacement of the stratum surface with time are respectively discussed. A machine learning method for predicting stratum surface deformation induced by shield tunnel excavation in complex strata is developed, where extreme learning machine (ELM), particle swarm optimization (PSO) algorithm and $k$ -fold cross-validation method are comprehensively considered. 65 data samples are collected from the field monitoring data of Qingdao Metro Line 4 and each data sample includes seven input values and one output value. The developed PSO-ELM has good prediction performance for stratum surface vertical displacement due to shield tunnel excavation. The case study in this work can provide a practical reference for similar tunneling projects.