Publications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13
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Item Strength prediction of a steel pipe having a hemi-ellipsoidal corrosion defect repaired by GFRP composite patch using artificial neural network(Elsevier, 2023) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Le Thanh, Coung; Mirjalili, Seyedali; Magd, Abdel WahabLocal stress concentration occurs when faults are present in pipelines under pressure. An example of such defects is the problem of corrosion caused by the environment in the field of pipeline installation. In the first part of this paper, we attempt to model the corrosion in the hemi-ellipsoidal form in order to study the locations of stress concentration in the specimens by several experimental cases and their influence on the stress resistance. The Gurson-Tvergaard-Needleman (GTN) mesoscopic damage model is used to simulate the specimens with good accuracy. In the second part, the investigation is extended to a pipe under static pressure with and without the presence of a glass fibre reinforced polymer (GFRP) composite patch. The maximum stress and percent stress reduction in a defected pipe with a hemi-ellipsoidal defect are determined using a 3D finite element model. This part examines the impact of the geometry of the composite patches on the percentage reduction of the maximum stresses in a section of pipeline subjected to static pressure. In the third part, the stresses and the percentage reduction in the maximum stresses are predicted using an artificial neural network (ANN). An inverse problem using ANN and Jaya algorithm is proposed to predict the group level of different sizes of defects under composite patches based on the maximum stress and percentage reduction of stress that the pipe withstands. The new method relates directly to real-world pipeline construction and repair applications. It could be also used for structural safety monitoringItem Weak stability bounds for approximations of invariant measures with applications to queueing(Springer, 2019) Issaadi, BadredineItem A cooperative learning strategy with multiple search mechanisms for improved artificial bee colony optimization(IEEE, 2015) Harfouchi, Fatima; Habbi, HaceneArtificial bee colony (ABC) optimization is a swarm based stochastic search strategy inspired by the foraging behavior of honeybees. Due to its simplicity and promising optimization capability, the ABC concept has devoted special interest with an increasing number of applications to scientific and engineering optimization problems. As an open research field, many researchers attempted to improve the performance of ABC algorithm through new algorithmic frameworks or by introducing modifications on the basic model. This paper presents an improved version of ABC algorithm based on a cooperative learning strategy with modified search mechanisms incorporated at both employed and onlooker levels. The proposed approach referred to as CLABC (Cooperative learning ABC) is tested on benchmark functions for numerical optimization. The results demonstrate the good performance and convergence of the proposed algorithm over other existing ABC variantsItem A fuzzy sliding mode robust control for a field oriented dual star induction machine fed by photovoltaic power supply with MPPT algorithm(2016) Sadouni, Radhwane; Meroufel, Abdelkader; Salim, Djeriou; Kheldoun, AissaItem Search of δ optimal and the algorithm of weighting in control adaptive(EuroJournals, 2009) Meglouli, HocineItem Optimization of defects in composite materials using an improved wavelet analysis basic algorithm(2011) Benhamou, Amina; Benyoucef, Boumedienne