An intelligent resilience evaluation model for the development of urban underground space with safety concern of surrounding existing built environment

The rationality and safety of Urban Underground Space (UUS) construction are critical aspects of urban development. Given the lack of safety concern of the surrounding environment in existing layout planning research for newly constructed UUS, this study proposed a novel resilience evaluation model for UUS safe development through the cross-application of geotechnical analysis, urban planning theory, and artificial intelligence (AI). By re-using geological information, the proposed model mapping algorithm enables the application of geological models to refined numerical calculations which can accurately locate the range of excavation disturbances of new UUS. Subsequently, the proposed AI algorithm is trained to learn from the existing UUS excavation cases, establishing a mathematical mapping relationship between the main construction parameters of UUS and the safety indexes of surrounding affected buildings. The mapping relationship enables the assessment of the development resilience of UUS in the planned area through the safety evaluation of surrounding affected buildings. Finally, the layout of an underground station for Nanjing Metro Line 13 is used, as an example, to apply the proposed intelligent model. This study contributes to the tools and methods for UUS layout evaluation from a safety perspective, and seeks the breakthrough for the cross-application of geotechnical science and urban planning theory.