Case study and risk assessment of water inrush disaster in Qingdao Metro Line 4

Abstract

Copyright © 2023 by the authors. Water inrush is one of the most frequent and catastrophic hazards in tunnel engineering, and poses serious threats to the safety of engineering and personnel. This paper presents a case study of a water inrush and ground collapse in the Qingdao Metro Line 4, which caused a cave-in with the diameter and depth of about 30 m and 6 m, respectively. Based on the field data and numerical modelling, the causes of the disaster were analyzed. A numerical model was used to analyze the changes of surface settlement, vault settlement and water pressure during the tunnel excavation. The results of the study indicate that the cause of this disaster was the failure of the tunnel vault surrounding rock caused by the weakening of the tunnel surrounding rock and water pressure, which in turn triggered the water inrush in the tunnel and caused a large volume of surface collapse. As the tunnel was excavated from the slightly weathered area to the strongly weathered area, the vault settlement increased, and the influence zone expanded towards the surface due to the continuous decrease in the strength of the surrounding rock. In particular, a negative pore water pressure zone was formed in a certain area around the tunnel during the water inrush. The negative pressure zone caused the surrounding groundwater to converge here, leading to an increase in the amount of water inflow, which also increased the scope and scale of the impact of this disaster. A risk assessment method for water inrush in tunnels is proposed. According to the geological and engineering characteristics of Qingdao area, the evaluation index system of tunnel water inrush risk was established. An RBF neural network was improved by gray correlation analysis and a PAM clustering algorithm to establish the tunnel water inrush risk assessment model. Comparing the evaluation data with the actual data, the prediction data of a traditional RBF neural network and a BP neural network, the accuracy and reliability of the model were verified. This study has value in reducing the occurrence of water inrush in a composite formation tunnel.