Digital Twin-Based Investigation of a Building Collapse Accident

Advances in Civil Engineering, 2022

引用方式: Zheng, Z., Liao, W., Lin, J., Zhou, Y., Zhang, C., Lu, X.* (2022). Digital Twin-Based Investigation of a Building Collapse Accident. Advances in Civil Engineering, 2022, 9568967. doi: 10.1155/2022/9568967 cited by count


工程结构的倒塌往往会带来重大人员伤亡及严重负面社会影响。倒塌事故调查对探明倒塌事故背后的关键诱因、机理,避免结构倒塌及提升工程韧性具有重要意义。然而,传统事故调查方法在现场数据与仿真模型同步更新等方面面临一系列困难,极大影响了事故调查的效率。因此,本研究提出了一种基于数字孪生的倒塌事故调查方法:1)首先,利用真实世界数据构建虚拟建筑模型;2)然后,构建高真实感多阶段事故仿真模型及其数据同步方法,实现事故全过程真实数据映射及虚拟仿真;3) 从而,基于数字孪生模型支持多学科专家知识集成、模型更新及校核;4)最后,利用相应模型揭示了倒塌事故的关键机理。研究以真实的倒塌事故案例为基础,验证了所提出的方法的有效性及可行性。

The collapse of engineering structures can cause significant casualties and have negative social effects. Collapse accident investigation can elucidate the potential causes and mechanisms of the collapse accident, thus remediating future structural collapse and enhancing the resilience. However, there are some obstacles to investigating complicated collapse accidents using conventional methods. For example, the out-syncs between on-site investigation and simulation analysis are intractable and can make discovering the cause of collapse accidents difficult. Hence, a digital twin-based investigation method for collapse accidents was proposed. First, basic virtual digital building models are established using real-world information. Then, after mapping the data from the real world into the virtual space, the corresponding highly realistic multistage models before and after the building collapse accident are constructed and synchronized. Using the digital twin method, investigators with multidisciplinary knowledge can efficiently integrate, update, and check the models. Finally, the potential collapse mechanism was revealed with the assistance of the corresponding models. To demonstrate the effectiveness of the proposed digital twin-based investigation method, a real collapse accident investigation is utilized as an example. These results validated our method.



This work was supported by the National Key R&D Program (no. 2019YFE0112800), the National Natural Science Foundation of China (no. 72091512), and the Tencent Foundation through the XPLORER PRIZE. The authors would like to acknowledge Professor Zuozhou Zhao (Tsinghua University) for providing on-site investigation data and materials.