The construction of deep foundation pits adjacent to existing subway tunnels faces enormous challenges, and once a safety accident occurs, there are often mass injuries that cause substantial economic losses. However, there are many shortcomings and defects in the traditional methods of assessing the safety of the pit itself and the existing tunnel in the construction of the deep foundation pit adjacent to existing tunnels. This study establishes an optimized complex network-based dynamic risk assessment model to dynamically assess the overall risk of deep foundation pits in adjacent existing tunnels systematically, solving the challenges of inaccurate risk assessment and inaccurate description of correlations between nonstationary time series data. In this study, we first divide the monitoring data into time windows and describe the correlation between nonstationary time-series monitoring data within each window based on the MF-DCCA method and the threshold method, and establish the adjacency matrix to prepare for the establishment of an optimized complex network model. Secondly, based on the adjacency matrix, a complex network model under different time windows is constructed, and risk assessment indexes are established through the topological parameters of the complex network model to explore the evolution of risk in time and space, so as to realize the risk distribution and quantitative evolution assessment of the system which is deep foundation pit adjacent to existing subway tunnels. Finally, the proposed method is tested by taking the Nanning underground comprehensive utilization project as an example. The results show that this method can quantify the risk of the construction of deep foundation pits adjacent to existing tunnels more effectively than the traditional method to describe the evolution law better. It has important guiding significance for strengthening the safety risk monitoring and safety management of the construction system of deep foundation pits adjacent to existing tunnels.
CITATION STYLE
Jiang, J., Liu, G., Huang, X., & Ou, X. (2022). The Research on Risk Distribution and Evolution of Deep Foundation Pit Construction Adjacent to Existing Tunnels Based on Complex Network. Shock and Vibration, 2022. https://doi.org/10.1155/2022/1685761
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