References
- Malomo, D., Scattarreggia, N., Orgnoni, A., Pinho, R., Moratti, M., & Calvi, G. M. (2020). Numerical Study on the Collapse of the Morandi Bridge. Journal of Performance of Constructed Facilities, 34(4). https://doi.org/10. 1061/(ASCE)CF. 1943-5509. 0001428
- Collapse of the Carola Bridge in Dresden — Institute of Concrete Structures — TU Dresden. (n.d.). Retrieved September 19, 2024, from https://tu-dres-den.de/bu/bauingenieurwesen/imb/das-institut/news/einsturz-der-carolabruecke-in-dres-den?set_language=en
- Farrar, C. R., & Worden, K. (2012). Structural Health Monitoring: A Machine Learning Perspective. Wiley. https://doi.org/10.1002/9781118443118
- Farrar, C. R., & Worden, K. (2007). An introduction to structural health monitoring. Philosophical Transactions of the Royal Society a Mathematical, Physical and Engineering Sciences, 365(1851), 303–315. https://doi.org/10.1098/rsta.2006.1928
- Fawad, M., Salamak, M., Poprawa, G., Koris, K., Jasinski, M., Lazinski, P., Piotrowski, D., Hasnain, M., & Gerges, M. (2023). Automation of structural health monitoring (SHM) system of a bridge using BIMification approach and BIM-based finite element model development. Scientific Reports, 13(1), 13215. https://doi.org/10.1038/s41598-023-40355-7
- Michael Grieves. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. White Paper, 1–7. https://doi.org/10.5281/zenodo.1493929
- Kapteyn, M. G., Knezevic, D. J., & Willcox, K. (2020, January 6). Toward predictive digital twins via component-based reduced-order models and interpretable machine learning. AIAA Scitech 2020 Forum. https://doi.org/10.2514/6.2020-0418
- Ye, X. W., Jin, T., & Yun, C. B. (2019). A review on deep learning-based structural health monitoring of civil infrastructures. Smart Structures and Systems, 24(5), 567–585. https://doi.org/10.12989/sss.2019.24.5.567
- O. Abdeljaber, O. Avci, S. Kiranyaz, M. Gabbouj, and D. J. Inman. (2017, Feb). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J Sound Vib, vol. 388, pp. 154–170. https://doi.org/10.1016/j.jsv.2016.10.043
- Hielscher, T., Khalil, S., Virgona, N., & Hadigheh, S. A. (2023). A neural network based digital twin model for the structural health monitoring of reinforced concrete bridges. Structures, 57, 105248. https://doi.org/10.1016/j.istruc.2023.105248
- Parola, M., Galatolo, F., Torzoni, M., Cimino, M., & Vaglini, G. (2022). Structural Damage Localization via Deep Learning and IoT Enabled Digital Twin. Proceedings of the 3rd International Conference on Deep Learning Theory and Applications, 199–206. https://doi.org/10.5220/0011320600003277.
- Wang, B., Li, Z., Xu, Z., Sun, Z., & Tian, K. (2023). Digital twin modeling for structural strength monitoring via transfer learning-based multi-source data fusion. Mechanical Systems and Signal Processing, 200, 110625. https://doi.org/10.1016/j.ymssp.2023.110625
- Yu, J., Song, Y., Tang, D., & Dai, J. (2021). A Digital Twin approach based on nonparametric Bayesian network for complex system health monitoring. Journal of Manufacturing Systems, 58, 293–304. https://doi.org/10.1016/j.jmsy.2020.07.005
- Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 21(4), 1906–1955. https://doi.org/10.1177/14759217211036880
- Liu, Y., Meng, X., Hu, L., Bao, Y., & Hancock, C. (2024). Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds. Sensors, 24(7), 2091. https://doi.org/10.3390/s24072091
- Armijo, A., & Zamora-Sánchez, D. (2024). Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study. Sensors, 24(7), 2115. https://doi.org/10.3390/s24072115
- Al-Hijazeen, A., & Koris, K. (2024). Smart Health Monitoring of Concrete Bridges Using Digital Twin and AI Applications. In Š. Nenadálová & P. Š. Johová (Eds.), 14th Central European Congress on Concrete Engineering (pp. 316–332).
- Árpád, Dr. O., Qéza, Dr. T., Péter, Dr. Ó., Péter, Dr. F., Dénes, Dr. D., György, Dr. F., Zsolt, Dr. H., György, Dr. B. L., György, E., László, Dr. V., Lajos, P., Zoltán, T., Zoltán, S., & Józsefné, M. (1990). Az M0 Autóút Soroksári Duna-Ág Hídja Medernyílásának Próbaterhelése. Budapest University of Technology and Economics – Reinforced Concrete Department.
- Al-Hijazeen, A., & Koris, K. (2024). Digital Twin Based Health Monitoring and Damage Detection of Reinforced Concrete Bridges. In Dr. B. L. GYÖRGY, S. Sólyóm, & S. Foste (Eds.), 15th fib International PhD Symposium in Civil Engineering (pp. 557–564). The International Federation for Structural Concrete (fib).
- Szinyéri, B., Kővári, B., Völgyi, I., Kollár, D., & Joó, A. L. (2023). A strain gauge-based Bridge Weigh-In Motion system using deep learning. Engineering Structures, 277, 115472. https://doi.org/10.1016/j.engstruct.2022.115472
- Aurélien Géron. (n.d.). Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow. O’Reilly Media. https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
- Mishra, M., Agarwal, A., & Maity, D. (2019). Neural-network-based approach to predict the deflection of plain, steel-reinforced, and bamboo-reinforced concrete beams from experimental data. SN Applied Sciences, 1(6), 584. https://doi.org/10.1007/s42452-019-0622-1
- Yoon, J., Lee, J., Kim, G., Ryu, S., & Park, J. (2022). Deep neural network-based structural health monitoring technique for real-time crack detection and localization using strain gauge sensors. Scientific Reports, 12(1), 20204. https://doi.org/10.1038/s41598-022-24269-4
- Zhang, G.-Q., Wang, B., Li, J., & Xu, Y.-L. (2022). The application of deep learning in bridge health monitoring: a literature review. Advances in Bridge Engineering, 3(1), 22. https://doi.org/10.1186/s43251-022-00078-7
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
- Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191
- Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
- Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A Comprehensive Survey on Transfer Learning. Proceedings of the IEEE, 109(1), 43–76. https://doi.org/10.1109/JPROC.2020.3004555
- Hegazy, T., Tully, S., & Marzouk, H. (1998). A neural network approach for predicting the structural behavior of concrete slabs. Canadian Journal of Civil Engineering, 25(4), 668–677. https://doi.org/10.1139/cjce-25-4-668
- Elshafey, A. A., Dawood, N., Marzouk, H., & Haddara, M. (2013). Predicting of crack spacing for concrete by using neural networks. Engineering Failure Analysis, 31, 344–359. https://doi.org/10.1016/j.engfailanal.2013.02.011
- Brinissat, M., Ray, R. P., & Kuti, R. (2023). Evaluation of the Szapáry Long-Span Box Girder Bridge Using Static and Dynamic Load Tests. Infrastructures, 8(5), 91. https://doi.org/10.3390/infrastructures8050091
- EN 1992-2: Eurocode 2: Design of concrete structures – Part 2: Concrete bridges - Design and detailing rules. (1992).