Performing fatigue state characterization in railway steel bridges using digital twin models
Railway infrastructures play a pivotal role in developing the national transportation system. Recently, the strategy of the railway engineer has been significantly shifted; along with the development of new assets, they tend to pay increasing attention to the operation and management of existing rai...
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| Published in: | Applied sciences Vol. 13; no. 11; pp. 1 - 22 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Basel
Multidisciplinary Digital Publishing Institute
01-06-2023
MDPI AG |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Railway infrastructures play a pivotal role in developing the national transportation system. Recently, the strategy of the railway engineer has been significantly shifted; along with the development of new assets, they tend to pay increasing attention to the operation and management of existing railway assets. In this regard, this paper proposes a Digital Twin (DT) model to improve fatigue assessment efficiency in the operational processes of railway steel bridges (RSBs). The DT concept mainly lies in the federation and interaction of a Fatigue Analysis System (FAS), which is based on Eurocodes principles, and a model in Building Information Modeling (BIM). Along with the proposed DT concept, a prototyping system for a real bridge is initiated and curated. The FAS is validated in good-agreement results with the ambient vibration test of the bridge (about 1.6% variation between numerical and experimental values), and close values were found between numerical and experimental stresses, the latter obtained by installing strain gauges on the bridge. The BIM model provides access to the numerical values of fatigue state results in a given bridge connection detail but also automatically represents that information in a 3D environment using a color-scale-based visualization process. Furthermore, a simulation model with the main input variables being the traffic and geometric conditions of the bridge is continuously updated for timely re-evaluation of the damage state, which shows promise for the lifecycle management of the bridge.
This work was financially supported by: Base Funding—UIDB/04708/2020 of the CONSTRUCT—Institute of R&D In Structures and Construction—funded by national funds through the FCT/MCTES (PIDDAC) and by national funds through FCT—Fundação para a Ciência e a Tecnologia; SFRH/BD/151229/2021. This work was also carried out in the framework of Shift2Rail projects IN2TRACK3 [101012456- H2020-S2RJU-CFM-2020] for the development of a DT framework. This work is also co-funded by the European Social Fund (ESF) through the Northern Regional Operational Programme (Norte 2020) [Funding Reference: NORTE-06-3559-FSE-000176]; And was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020.
The authors would like to acknowledge ANI (“Agência Nacional de Inovação”) for the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference POCI-01-0247-FEDER-069642, co-financed by European Regional Development Fund (FEDER) through Operational Competitiveness and Internationalization Program (POCI)”. |
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| ISSN: | 2076-3417 2076-3417 |
| DOI: | 10.3390/app13116741 |