Dynamic risk assessment is a pivotal tool for enhancing construction safety and minimizing the potential for partial failure during deep and extensive excavation projects. To enhance the efficacy of dynamic risk assessment in deep excavation, this study introduces a novel risk assessment model designed to evaluate instability risk in extensive excavations. It comprises a risk factor selection model for identifying the most pertinent factors and an instability risk assessment model for gauging the extent of instability risk throughout the construction process. Then, the model was deployed in the construction of Anshan Road Station of the Qingdao Metro. To pinpoint the factors with the most pronounced impact on excavation instability, a risk factor selection model was employed, yielding a comprehensive risk evaluation index system. For real-time assessment of risk, the monitoring data were used as the primary source of evidence. A comprehensive comparative analysis involving actual data and predictions from conventional RBF and back propagation neural networks was performed. The outcome of this analysis underscored the superior accuracy and predictive capabilities of the assessment model. The instability risk assessment model offers the ability to dynamically evaluate the instability risk associated with extensive excavations featuring a combination of soil and rock. It can serve as a valuable methodological tool, furnishing essential support for the systematic prevention and mitigation of excavation instability disasters.

The tunnel collapse occurred during the construction of the Qingdao Metro Line Ⅰ, resulting in a pit measuring approximately 10 m in diameter and 7 m in depth on the ground surface. The collapse inflicted substantial economic losses, casualties, and subsequent disruptions to ongoing construction activities. To clarify the causes of this collapse and investigate the underlying factors, a comprehensive approach combining field surveys and numerical simulations was undertaken. The results show that the collapse was primarily attributed to inadequate support when facing the challenging geological conditions, poor surrounding rocks (Grade VI) and a high volume of groundwater. The combination of abundant water and sand seeped into the tunnel, culminating in a catastrophic ground collapse. Notably, the arch roof experienced extensive deformation and damage due to the significant seepage pressure, which was exacerbated by the absence of timely support. Then, ground grouting was promptly conducted to repair the affected area and reinforce the tunnel structure. Subsequent monitoring of surface deformations following the resumption of tunneling operations indicated the effectiveness of the disaster treatment approach. These achievements constitute a reliable theoretical foundation for similar projects, particularly in terms of environmental protection and ensuring safe construction practices.

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.