Machine learning for predicting pipeline displacements based on soil rigidity
- Author
- Meriem Seguini, Samir Khatir (UGent) , Djamel Nedjar and Magd Abdel Wahab (UGent)
- Organization
- Abstract
- This study investigates the impact of the soil rigidity on the mechanical behaviour for linear and nonlinear pipelines. The work is based on the results of a series of mechanical finite element analyses based on the VanMarcke and Artificial Neural Network (ANN). The numerical model is validated based on the literature. Different simulations have been generated to obtain data response of the pipe based on displacement. The predicted results using ANN are compared with VanMarcke to prove the effectiveness and the importance of the ANN. The analysis proves that the variation of the coefficient of subgrade reaction can induce a significant displacement of the pipe. The results prove that ANN provides a major role in the evolution of the real displacement of the pipeline and allows us to obtain more precise and interesting results based on both linear and nonlinear cases.
Downloads
-
(...).pdf
- full text (Published version)
- |
- UGent only
- |
- |
- 733.42 KB
-
FFW1284.pdf
- full text (Accepted manuscript)
- |
- open access
- |
- |
- 991.03 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GKRAH33S9FT6XBTH0QJQMTS3
- MLA
- Seguini, Meriem, et al. “Machine Learning for Predicting Pipeline Displacements Based on Soil Rigidity.” Proceedings of the 10th International Conference on Fracture Fatigue and Wear, FFW 2022, Springer, 2023, pp. 29–40, doi:10.1007/978-981-19-7808-1_4.
- APA
- Seguini, M., Khatir, S., Nedjar, D., & Abdel Wahab, M. (2023). Machine learning for predicting pipeline displacements based on soil rigidity. Proceedings of the 10th International Conference on Fracture Fatigue and Wear, FFW 2022, 29–40. https://doi.org/10.1007/978-981-19-7808-1_4
- Chicago author-date
- Seguini, Meriem, Samir Khatir, Djamel Nedjar, and Magd Abdel Wahab. 2023. “Machine Learning for Predicting Pipeline Displacements Based on Soil Rigidity.” In Proceedings of the 10th International Conference on Fracture Fatigue and Wear, FFW 2022, 29–40. Singapore: Springer. https://doi.org/10.1007/978-981-19-7808-1_4.
- Chicago author-date (all authors)
- Seguini, Meriem, Samir Khatir, Djamel Nedjar, and Magd Abdel Wahab. 2023. “Machine Learning for Predicting Pipeline Displacements Based on Soil Rigidity.” In Proceedings of the 10th International Conference on Fracture Fatigue and Wear, FFW 2022, 29–40. Singapore: Springer. doi:10.1007/978-981-19-7808-1_4.
- Vancouver
- 1.Seguini M, Khatir S, Nedjar D, Abdel Wahab M. Machine learning for predicting pipeline displacements based on soil rigidity. In: Proceedings of the 10th International Conference on Fracture Fatigue and Wear, FFW 2022. Singapore: Springer; 2023. p. 29–40.
- IEEE
- [1]M. Seguini, S. Khatir, D. Nedjar, and M. Abdel Wahab, “Machine learning for predicting pipeline displacements based on soil rigidity,” in Proceedings of the 10th International Conference on Fracture Fatigue and Wear, FFW 2022, Ghent, Belgium, 2023, pp. 29–40.
@inproceedings{01GKRAH33S9FT6XBTH0QJQMTS3,
abstract = {{This study investigates the impact of the soil rigidity on the mechanical behaviour for linear and nonlinear pipelines. The work is based on the results of a series of mechanical finite element analyses based on the VanMarcke and Artificial Neural Network (ANN). The numerical model is validated based on the literature. Different simulations have been generated to obtain data response of the pipe based on displacement. The predicted results using ANN are compared with VanMarcke to prove the effectiveness and the importance of the ANN. The analysis proves that the variation of the coefficient of subgrade reaction can induce a significant displacement of the pipe. The results prove that ANN provides a major role in the evolution of the real displacement of the pipeline and allows us to obtain more precise and interesting results based on both linear and nonlinear cases.}},
author = {{Seguini, Meriem and Khatir, Samir and Nedjar, Djamel and Abdel Wahab, Magd}},
booktitle = {{Proceedings of the 10th International Conference on Fracture Fatigue and Wear, FFW 2022}},
isbn = {{9789811978074}},
issn = {{2195-4356}},
language = {{eng}},
location = {{Ghent, Belgium}},
pages = {{29--40}},
publisher = {{Springer}},
title = {{Machine learning for predicting pipeline displacements based on soil rigidity}},
url = {{http://doi.org/10.1007/978-981-19-7808-1_4}},
year = {{2023}},
}
- Altmetric
- View in Altmetric