- Authors
- C. Ying; Long Chen; Daguang Han; Kai-Xin Hu; Yu Zhang; Guoqian Ren; Yanhui Liu; Yongquan Dong; Yatong Yuan
- Journal
- Handbook of Digital Twins, 2024
- DOI
- 10.1201/9781003425724-47
- Citations
- 0 (OpenAlex, 3 Aug 2026)
Key findings
- Using a developed method, the deviation of the arch bridge alignment in the horizontal and lateral directions does not exceed 2.5 mm; the deviation of the alignment in the vertical direction does not exceed 1.4 cm; and the maximum displacement difference in the construction process does not exceed 7/8,000 of the calculated span diameter.
Abstract
The use of digital twin for structural object description and simulation has been proven to be highly accurate, fast updating, and well automated. This chapter attempts to develop a digital twin–based method for the construction control of machining precision and installation quality of structural steel rigid skeleton (SSRS) bridge. The method involves using the building information modeling model as the digital twin model (DTM) carrier and 3D laser scanning technology as the measurement and verification means to continuously update and verify the SSRS to form the geometric digital twin model (GDTM) of the SSRS. This highly accurate GDTM is used to control the manufacturing dimensions of the SSRS section as well as the lifting position of the construction section. Using a developed method, the deviation of the arch bridge alignment in the horizontal and lateral directions does not exceed 2.5 mm; the deviation of the alignment in the vertical direction does not exceed 1.4 cm; and the maximum displacement difference in the construction process does not exceed 7/8,000 of the calculated span diameter. These findings show that using the high-precision GDTM to control the accuracy of arch bridge stiffener processing and installation has the advantages of high accuracy, high efficiency, and low cost.
Cite this work
@article{han2024developingaconst,
title = {Developing a Construction Digital Twin for Bridges},
author = {C. Ying and Long Chen and Daguang Han and Kai-Xin Hu and Yu Zhang and Guoqian Ren and Yanhui Liu and Yongquan Dong and Yatong Yuan},
journal = {Handbook of Digital Twins},
year = {2024},
doi = {10.1201/9781003425724-47},
}
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Author identity: ORCID 0000-0003-3787-963X · Google Scholar