Parallel simulation and prediction techniques for digital twins in urban underground spaces

Abstract

With the advancement of intelligent systems and the increasing utilization of underground spaces, digital twin technology has become pivotal in enhancing the efficiency and safety of subterranean operations and maintenance. The concept of parallel simulation and prediction (PSP) technology, a critical component in the realization of digital twins, along with its associated research challenges, requires further elucidation. This paper offers a comprehensive overview of current research on PSP within the context of digital twins for urban underground spaces. The 62 papers meeting the inclusion and exclusion criteria are categorized into two key areas: model updating and future evolution prediction. Key challenges identified include the need for regular updates in geometric models, the demand for real-time predictive analytics, and data-related issues in information modeling. Finally, future research directions are outlined, focusing on the automatic interpretation of detection data, self-updating digital twin models, and multi-source heterogeneous data integration technologies.