Feedforward Neural Network-Based Digital Twin for SHM of Bridges
This study introduces a digital twin framework combining finite element (FE) modelling with feedforward neural networks (FNNs) for structural health monitoring (SHM) of reinforced concrete bridges via a case study. A detailed FE model of a post-tensioned bridge was developed using data from the impl...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2025-07-01
|
| Series: | Architecture, Civil Engineering, Environment |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/acee-2025-0026 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849737758230183936 |
|---|---|
| author | AL-HIJAZEEN Asseel KORIS Kálmán |
| author_facet | AL-HIJAZEEN Asseel KORIS Kálmán |
| author_sort | AL-HIJAZEEN Asseel |
| collection | DOAJ |
| description | This study introduces a digital twin framework combining finite element (FE) modelling with feedforward neural networks (FNNs) for structural health monitoring (SHM) of reinforced concrete bridges via a case study. A detailed FE model of a post-tensioned bridge was developed using data from the implementation plan and structural conditions. The FE model was verified and validated through extensive load testing, achieving 95% accuracy. Displacements, strains, stresses and crack widths in critical points of the structure, and virtual sensor data, were extracted from the FE model for various load scenarios, to simulate the behaviour and measurements on the real bridge. An FNN was then trained using longitudinal displacements and strain data as inputs to predict key performance indicators, such as deflections, stresses and crack width. The overall digital twin achieved prediction accuracy between 90-93%. The established connection was applied on the real bridge, where sensor data were fed into the FNN model, allowing prediction of structural performance for SLS once appropriate limits are set. This approach offers an automated solution for real-time SHM and proactive bridge maintenance. |
| format | Article |
| id | doaj-art-747e3d32da2d455eb3ef1730e36f27c6 |
| institution | DOAJ |
| issn | 2720-6947 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Architecture, Civil Engineering, Environment |
| spelling | doaj-art-747e3d32da2d455eb3ef1730e36f27c62025-08-20T03:06:50ZengSciendoArchitecture, Civil Engineering, Environment2720-69472025-07-0118215716910.2478/acee-2025-0026Feedforward Neural Network-Based Digital Twin for SHM of BridgesAL-HIJAZEEN Asseel0KORIS Kálmán1MSc; Budapest University of Technology and Economics, Faculty of Civil Engineering, Műegyetem rkp. 3, 1111Budapest, HungaryAssoc. Prof.; Budapest University of Technology and Economics, Faculty of Civil Engineering, Műegyetem rkp. 3, 1111Budapest, HungaryThis study introduces a digital twin framework combining finite element (FE) modelling with feedforward neural networks (FNNs) for structural health monitoring (SHM) of reinforced concrete bridges via a case study. A detailed FE model of a post-tensioned bridge was developed using data from the implementation plan and structural conditions. The FE model was verified and validated through extensive load testing, achieving 95% accuracy. Displacements, strains, stresses and crack widths in critical points of the structure, and virtual sensor data, were extracted from the FE model for various load scenarios, to simulate the behaviour and measurements on the real bridge. An FNN was then trained using longitudinal displacements and strain data as inputs to predict key performance indicators, such as deflections, stresses and crack width. The overall digital twin achieved prediction accuracy between 90-93%. The established connection was applied on the real bridge, where sensor data were fed into the FNN model, allowing prediction of structural performance for SLS once appropriate limits are set. This approach offers an automated solution for real-time SHM and proactive bridge maintenance.https://doi.org/10.2478/acee-2025-0026artificial neural networksdigital twinfeedforward neural networksfinite element modellingreinforced concrete bridgessensor datastructural health monitoring |
| spellingShingle | AL-HIJAZEEN Asseel KORIS Kálmán Feedforward Neural Network-Based Digital Twin for SHM of Bridges Architecture, Civil Engineering, Environment artificial neural networks digital twin feedforward neural networks finite element modelling reinforced concrete bridges sensor data structural health monitoring |
| title | Feedforward Neural Network-Based Digital Twin for SHM of Bridges |
| title_full | Feedforward Neural Network-Based Digital Twin for SHM of Bridges |
| title_fullStr | Feedforward Neural Network-Based Digital Twin for SHM of Bridges |
| title_full_unstemmed | Feedforward Neural Network-Based Digital Twin for SHM of Bridges |
| title_short | Feedforward Neural Network-Based Digital Twin for SHM of Bridges |
| title_sort | feedforward neural network based digital twin for shm of bridges |
| topic | artificial neural networks digital twin feedforward neural networks finite element modelling reinforced concrete bridges sensor data structural health monitoring |
| url | https://doi.org/10.2478/acee-2025-0026 |
| work_keys_str_mv | AT alhijazeenasseel feedforwardneuralnetworkbaseddigitaltwinforshmofbridges AT koriskalman feedforwardneuralnetworkbaseddigitaltwinforshmofbridges |