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http://dx.doi.org/10.13161/kibim.2019.9.3.001

Digital Twin Model of a Beam Structure Using Strain Measurement Data  

Han, Man-Seok (인하대학교 토목공학과)
Shin, Soo-Bong (인하대학교 사회인프라공학과)
Moon, Tae-Uk (인하대학교 토목공학과)
Kim, Da-Un (인하대학교 토목공학과)
Lee, Jong-Han (인하대학교 사회인프라공학과)
Publication Information
Journal of KIBIM / v.9, no.3, 2019 , pp. 1-7 More about this Journal
Abstract
Digital twin technology has been actively developed to monitor and assess the current state of actual structures. The digital twin changes the traditional observation method performed in the field to the real-time observation and detection system using virtual online model. Thus, this study designed a digital twin model for a beam and examined the feasibility of the digital twin for bridges. To reflect the current state of the bridge, model updating was performed according to the field test data to construct an analysis model. Based on the constructed bridge analysis model, the relationship between strain and displacement was used to represent a virtual model that behaves in the same way as the actual structure. The strain and displacement relationship was expressed as a matrix derived using an approximate analytical theory. Then, displacements can be obtained using the measured data obtained from strain sensors installed on the bridge. The coordinates of the obtained displacements are used to construct a virtual digital model for the bridge. For verification, a beam was fabricated and tested to evaluate the digital twin model constructed in this study. The displacements obtained from the strain and displacement relationship agrees well with the actual displacements of the beam. In addition, the displacements obtained from the virtual model was visualized at the locations of the strain sensor.
Keywords
Beam; Digital Twin; Displacement; Strain;
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  • Reference
1 AUGMATE. Digital Twin City: Virtual Singapore, https://augmate.io (Apr. 14. 2019).
2 BCG. (2017). The Future of Construction. The Boston Consulting Group, pp. 1-50.
3 Davos, K. World Economic Forum Annual Meeting, Switzerland, https://www.weforum.org (Mar. 4. 2018).
4 GE Digital. Minds + Machines: Meet A Digital Twin, https://www.youtube.com (Mar. 4. 2018).
5 Kay, S., Michael, T., Kosmas, D. (2017). IFC-based modeling of cyber-physical systems in civil engineering. The 24rd International Workshop on Intelligent Computing in Engineering, At Nottingham, UK, pp. 269-278.
6 Korea Concrete Institute. (2010). Safety Evaluation Criteria (Plan) and Examples for Commonly Used Concrete Bridges, pp. 104-114.
7 LG CNS. Why does Singapore make the entire country a virtual reality?. LG CNS, https://blog.lgcns.com/1749 (Jul. 3. 2018)
8 Michael, S., Juergen, R. (2016). From Simulation to Experimentable Digital Twins : Simulation - based Development and Operation of Complex Technical Systems. IEEE, Edinburgh, UK. doi:10.1109/SysEng.2016.7753162, pp. 273-278.   DOI
9 Ministry of Land, Infrastructure and Transport. (2015). the Limit-State based Bridge Design Specification Chapter 3rd, pp. 14-16.
10 NIC. NATIONAL INFRASTRUCTURE COMMISSION, https://www.nic.org.uk (Feb. 17. 2018).
11 Orlando, F. GartnerIdentifiesthe Top 10 Strategic Technology Trends for 2017, https://www.gartner.com (Mar. 4. 2018).
12 IBM Watson Internet of Things. Introduction to Digital Twin: Simple, but detailed, https://www.youtube.com (Mar. 6. 2018).
13 Ryan, M., Lee, J., Padmesh, M., Peyman, D., Omkar, K., Sivasubramani, K., ... Anand, P. (2017). A Simulation-Based Digital Twin for Model-Driven Health Monitoring and Predictive Maintenance of an Automotive Braking System. The 12th International Modelica Conference, Prague, Czech Republic. doi: 10.3384/ecp1713235, pp. 35-46.   DOI
14 Wikipedia. Cyber-physical system, https://en.wikipedia.org (Mar. 15. 2018).
15 Xianming, L., Ping, L., Yan, L., Hong, R. (2017). Leak Location of Pipeline with Multibranch Based on a Cyber-Physical System. information, 8, 113. doi: 10.3390/info8040113   DOI
16 Han, M. (2019). Construction of Digital Twin for Bridges and Development of Damage Localization Process using CNN Deep Learning, Masters Thesis, Inha University, pp. 12-49.