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.
The sustainable development of old industrial buildings is in line with the national construction strategy and has an important impact on current urban renewal. Only by achieving a unified balance among economic, social, and environmental factors can reused industrial buildings be considered sustainable. However, there are no relevant sustainability assessment indicators and methods for reused industrial buildings in China. The purpose of this study was to provide a reasonable and effective method for assessing the sustainability of reused industrial buildings. First, this study analysed the factors influencing reused industrial building sustainability through a project investigation. Second, based on the assessment indicator setting procedure, the sustainability assessment indicator system for reused industrial buildings was optimised. Moreover, a multi-level sustainability assessment model based on extenics was established to identify the correlation functions of indicators with different attributes. Finally, a case was considered to verify this assessment method. The results showed that this assessment method in good agreement with the actual state of the case was validated to be more effective and practical. The assessment method could provide a basis for decision-making to improve sustainability and could be adopted by relevant rating agencies to determine the sustainability level of reused industrial buildings.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.