Digital twin approach for enhancing urban resilience: A cycle between virtual space and the real world

Abstract

Construction of disaster-resilient cities has attracted considerable attention. However, traditional methods of studying urban disaster resilience through experimental approaches are often constrained by various limitations, such as testing sites, costs and ethical considerations. To address these constraints, this paper proposes incorporating digital twin concepts into urban disaster resilience research. By establishing a connection between the physical realm of the city and its virtual counterpart, this approach utilizes digital simulations to overcome the limitations of experimental methods and enables dynamic deduction and control of the disaster process. This paper delves into three key aspects encompassing the acquisition of data from reality to the virtual space, disaster simulation within the virtual space, and translation of virtual insights into effective disaster prevention strategies in reality. It provides a comprehensive summary of relevant research endeavors from the authors’ research group and showcases the effectiveness and potential of the proposed techniques. These findings serve as references for pre-disaster planning, real-time emergency assessments, post-disaster rescue operations, and accident investigations for buildings and cities.

Keywords

Digital twins / Disaster simulation / Resilience / Disaster prevention and mitigation

Relevance to resilience

This work addresses the critical applications of digital twin in research on building and urban resilience. The use of digital twin directly contributes to an increase in the resilience of buildings and cities, demonstrating significant effectiveness in pre-disaster planning, real-time emergency assessments, and other aspects.

CRediT authorship contribution statement

Yixing Wang: Writing - original draft, Writing - review & editing. Qingrui Yue: Supervision. Xinzheng Lu: Supervision. Donglian Gu: Methodology, Software, Visualization, Writing - review & editing. Zhen Xu: Methodology. Yuan Tian: Methodology. Shen Zhang: Methodology.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 52238011, 52208456), China National Postdoctoral Program for Innovative Talents (BX20220031), and Shenzhen Science and Technology Program (ZDSYS20210929115800001).

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