Crack Identification in Pipe Using Improved Artificial Neural Network

Seguini, Meriem; Khatir, Tawfiq; Khatir, Samir; Boutchicha, Djilali; Nedjar, Djamel; Wahab, Magd Abdel

doi:10.1007/978-981-19-4835-0_2

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, at later stage other probabilistic and optimization methods (the genetic algorithm the eXtended Iso Geometric analysis (XIGA) The Neural Artificial Network ANN-PSO) have been developed and used to identify the damage crack in different types of structures (beam and pipe) [20][21][22][23] Where the obtained results proved the efficiency of the ANN-PSO method on the crack identification. The objective of this paper is in particular to expand the influence (effects) of the variation of the soil subgrade reaction on the response of the pipeline where the study is based on the results of a series of FEM obtained by using the VanMarcke method [24] which is compared with the data generated by ANN technique.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predicting Pipeline Displacements Based on Soil Rigidity

Seguini

Khatir

Nedjar

et al. 2022

Proceedings of the 10th International Conference on Fracture Fatigue and Wear

View full text Add to dashboard Cite

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 probabilistic analysis. 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 serves 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.

show abstract