Development and validation of graphics‐based digital twin framework for UAV‐aided post‐earthquake inspection of high‐rise buildings

Jingjing Wang, Yongjingbang Wu, Shuo Wang, Yasutaka Narazaki, Hai Liu, Billie F. Spencer
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Abstract

SummaryTraditional post‐earthquake inspection of civil infrastructure is conducted manually, taking a considerable amount of time and often putting inspectors in harm's way. This problem is exacerbated in modern cities, where millions of people can be left homeless until their residences are deemed safe to reinhabit. Image collection enabled by commercial unmanned aerial vehicles (UAVs) combined with computer vision‐based techniques has provided an alternative with high potential for rapid post‐earthquake inspection. However, the extracted images of the damage alone are inadequate to evaluate the system‐level safety condition of a structure. The quality of the visual information also heavily relies on the effectiveness of the UAV inspection scheme which is susceptible to environmental uncertainties. To this end, a graphics‐based digital twin (GBDT) framework is developed for UAV‐aided post‐earthquake inspection of high‐rise buildings and validated using a high‐rise building in Guangzhou, China. The GBDT is comprised of a finite element (FE) model and a photorealistic computer graphics (CG) model, with the latter being informed by the former, jointly providing as a comprehensive virtual representation of the structure so that every step of the post‐earthquake inspection procedure can be planned and evaluated virtually. First, to avoid the cumbersome nature of constructing the graphical representation of the numerous components in high‐rise buildings, the CG model in the GBDT is created by automatically importing structural components from the FE model and adding nonstructural components according to the dimensions of the as‐built structure. This fast modeling process as well as the accuracy of the virtual presentation are validated by point cloud comparisons between the CG model and the as‐built structure. Subsequently, the GBDT is used to showcase the evaluation of UAV flight schemes for post‐earthquake inspection of high‐rise buildings. To shorten flight time and place more emphasis on potential damage, FE analysis is conducted to determine the earthquake‐induced damage locations. Consistent damage hotspots are then marked on the CG model, along with restrictions from the real environment such as obstacles, weak satellite signal, wind speed, and lighting conditions considered in the synthetic environment. Finally, applying the synthetic environment as the testbed, three UAV‐aided inspection schemes are implemented virtually and the best UAV flight scheme is determined for the assumed field inspection. This example demonstrates the flexibility of the GBDT in representing the real‐world structure and environmental conditions and its efficacy in assisting decision making for rapid and effective structural inspection in the aftermath of an earthquake.
为无人机辅助高层建筑震后检测开发和验证基于图形的数字孪生框架
摘要传统的震后民用基础设施检查都是人工进行的,不仅耗费大量时间,检查人员还经常处于危险之中。在现代城市中,这一问题更加严重,数百万人可能无家可归,直到他们的住所被认为可以重新居住。商用无人飞行器(UAV)结合计算机视觉技术实现的图像采集为震后快速检查提供了一种极具潜力的替代方法。然而,仅靠提取的受损图像不足以评估结构的系统级安全状况。视觉信息的质量还在很大程度上取决于无人机检测方案的有效性,而无人机检测方案很容易受到环境不确定性的影响。为此,我们开发了一个基于图形的数字孪生(GBDT)框架,用于无人机辅助的高层建筑震后检测,并利用中国广州的一栋高层建筑进行了验证。GBDT 由一个有限元(FE)模型和一个逼真的计算机图形(CG)模型组成,后者由前者提供信息,共同提供一个全面的结构虚拟表征,以便对震后检测程序的每一步进行虚拟规划和评估。首先,为避免高层建筑中众多构件图形化表达的繁琐,GBDT 中的 CG 模型是通过自动从 FE 模型中导入结构构件,并根据竣工结构的尺寸添加非结构构件来创建的。CG 模型与竣工结构之间的点云对比验证了这一快速建模过程以及虚拟展示的准确性。随后,GBDT 被用于展示高层建筑震后检测无人机飞行方案的评估。为了缩短飞行时间并更加重视潜在的损坏,我们进行了有限元分析,以确定地震引起的损坏位置。然后在 CG 模型上标出一致的破坏热点,同时考虑到合成环境中的障碍物、弱卫星信号、风速和光照条件等真实环境的限制。最后,将合成环境作为测试平台,虚拟实施三种无人机辅助检测方案,并为假定的现场检测确定最佳无人机飞行方案。这个例子展示了 GBDT 在表现真实世界结构和环境条件方面的灵活性,以及它在地震后协助决策制定快速有效的结构检测方面的功效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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