# Performing Fatigue State Characterization in Railway Steel Bridges Using Digital Twin Models
## 5 Conclusions  
This work addressed the development of a DT model to increase the flexibility of the fatigue evolution assessment process for railway steel bridges. For this purpose, a Fatigue Analysis System (FAS) and BIM modeling were combined. The FAS incorporates a numerical finite element part, implemented in ANSYS(r) v.2021 software, to obtain part of the input parameters for fatigue computation and the other function related to fatigue damage computation, based on the Linear Damage Accumulation Method (LEAM) proposed in the Eurocodes, and using MATLAB(r) v2021a software for its implementation. The Varzea bridge in Portugal was chosen for model implementation, and the connection details on the diagonals of the bridge truss structure were evaluated. Four scenarios involving the normative traffic specified in EC1-2 and changes to the geometric properties of some truss members were tested as part of this evaluation, expecting to evaluate more scenarios and involve other connections details within the scope of this ongoing research. The results achieved are promising, highlighting the following issues to improve the performance of fatigue assessment:  
* Development of a data flow scheme for interaction between the FAS and BIM model for fatigue evolution representation and visualization;
* Creation of an open system that allows inputs related to various traffic conditions and geometric properties, resulting in an automatic representation of fatigue evolution;
* Flexibilization in global fatigue evaluation through automatic mapping of fatigue evolution, allowing for a quick decision-making process regarding the need or not for advanced local analysis or inspection levels to be implemented.  
Conceptualization, I.A.N., N.-S.D. and C.S.H.; methodology, I.A.N. and C.S.H.; software, I.A.N. and C.S.H.; formal analysis, I.A.N.; investigation, I.A.N.; resources, I.A.N. and C.S.H.; data curation, I.A.N. and C.S.H.; writing--original draft preparation, I.A.N.; writing--review and editing, N.-S.D. and J.P.M.; visualization, I.A.N.; supervision, J.A.M. and R.C.; project administration, J.A.M. and R.C.; funding acquisition, N.-S.D., J.A.M. and RC. All authors have read and agreed to the published version of the manuscript.  
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--Fundacao para a Ciencia e a Tecnologia; SFRH/BD/151229/2021. This work was also carried out in the framework of Shift2 Rail projects IN2 TRACK3 [101012456- H2020- S2 RJU-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.  
Not applicable.  
Figure 20: Example of information check at a connection detail.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical and privacy concerns.  
**Acknowledgments:** The authors would like to acknowledge ANI (\"Agencia Nacional de Inovacao\") 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)\".  
**Conflicts of Interest:** The authors declare no conflict of interest.