Abstract
With the increasing exploitation and utilization of underground spaces, the excavation of deep foundation pits adjacent to existing metro tunnels is becoming increasingly common. These excavations have the potential to cause safety problems for the operation of the nearby metro. Therefore, to prevent metro tunnel accidents from occurring during the construction process and to ensure the safety of lives and property, it is necessary to establish a risk‐based early warning system. During the excavation process, the main methods for preventing accidents in excavations adjacent to existing metro tunnels are manual analyses based on on‐site monitoring data. However, these methods make it difficult to enact effective control measures in a timely manner owing to the lag of information processing. However, the trial application of artificial neural networks (ANNs) and building information modelling (BIM) for engineering projects provides a new method for solving such problems. This study uses a backpropagation neural network to predict the real‐time deformation of the tunnel based on monitoring data from the adjacent construction site. A safety risk assessment model is then established based on the relevant specifications. Through the establishment of an intelligent warning system, the safety risk to the metro tunnel during the construction process can be displayed in a three‐dimensional (3D) form using the BIM. The operation results of the ANN–BIM system show that it can effectively present the safety risk to existing metro tunnels in a 3D manner, which can provide managers with rapid and convenient visual information to inform their decision‐making.
Highlights
With the building of numerous metro tunnels in cities and continuous urban construction and development, available land is becoming increasingly scarce, resulting in more frequent excavation of deep foundation pits adjacent to existing metro tunnels [1, 2]
Establishment of the Safety Risk Early Warning System. e safety risk early warning system employs building information modelling (BIM) as the platform, wireless sensors and manual monitoring as the data collection methods, the prediction model for deformation of the adjacent existing tunnel caused by deep foundation pit construction as the basis for data processing, and the allowable deformation limits given in tunnel-related specifications as the early warning thresholds. e data monitoring module, real-time tunnel deformation prediction module, safety risk assessment module, and safety risk early warning module are integrated into Navisworks with the help of application programming interface (API) development to establish the early warning system. is system is connected to the BIM collaborative management platform to realize data sharing. e system
Blue Green Yellow Red has the ability to provide dynamic, visual, and real-time risk warnings. is allows for optimization of the safety risk warning method for excavation adjacent to existing metro tunnels, which can ensure a smooth construction process and safe operation of the existing metro tunnels. e overall operation framework of the built early warning system is shown in Figure 3, and a specific demonstration of the early warning system will be given through a case study (Section 4)
Summary
With the building of numerous metro tunnels in cities and continuous urban construction and development, available land is becoming increasingly scarce, resulting in more frequent excavation of deep foundation pits adjacent to existing metro tunnels [1, 2]. Li et al [15] proposed a Chinese metro construction safety risk identification system and early warning system based on the BIM platform. Is study adopts the BIM platform for visualization, employs an intelligent monitoring system and manual monitoring as data sources, and utilizes the strong prediction capability of a BP neural network to predict safety risks. By connecting to a mobile phone terminal through the network, the proposed system can notify managers in a timely manner when safety risks occur, to achieve the goal of providing real-time visualized warnings. E safety risk early warning subsystem (SREWS) uses the BIM information model as a platform and integrates the other three systems on this platform through application programming interface (API) development to realize visual early warnings.
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