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Author: search.filters.author.Ben Seghier, Mohamed El AmineSubject: search.filters.subject.Corrosion

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    Probabilistic investigation on the reliability assessment of mid- and high-strength pipelines under corrosion and fracture conditions
    (Elsevier, 2020) Guillal, Abdelkader; Ben Seghier, Mohamed El Amine; Abdelbaki, Noureddine; Correia, José A.F.O.; Zahiraniza, Mustaffa; Nguyen-Thoi, Trung
    In order to reduce the economic costs of pipeline construction projects and for offering a good combination of strength and toughness for efficient transportation of large quantities of hydrocarbon products under high pressure, High Strength Steels (HSS) such as API 5L X70 to X120 are used recently in the construction of pipeline systems for the large oil and gas projects. The commonly utilized models for the reliability evaluation of the HSS pipelines may lead to some conservatism regarding the used data. This paper aims to evaluate the system reliability of HSS pipelines with combined corrosion and cracks defects. Therefore, two failure modes as the plastic collapse and fracture are considered. The effect of different correlations under the term of the strain-hardening exponent that depends on the yield to ultimate tensile strength (Y/T) ratio is investigated. The reliability index of HSS pipelines is evaluated separately for each failure mode using the subset simulation technique. Herein, the tensile strength proprieties of the HSS pipelines are taken into consideration, while the applied methodology utilizes novel probabilistic models to predict the burst pressure for the plastic collapse failure mode. The steels toughness is taken as equal to the minimum requirement for both the ductile and the brittle fracture arrest applied in the HSS pipelines. Moreover, the reliability of the system with multiple failure modes is evaluated to show the mutual existence effect of crack and corrosion defects on pipeline safety
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    Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete
    (Springer, 2020) Ben Seghier, Mohamed El Amine; Ouaer, Hocine; Ghriga, Mohammed Abdelfetah; Nait Amar, Menad; Duc-Kien, Thai
    The capacity efficiency of load carrying with the accurate serviceability performances of reinforced concrete (RC) structure is an important aspect, which is mainly dependent on the values of the ultimate bond strength between the corroded steel reinforcements and the surrounding concrete. Therefore, the precise determination of the ultimate bond strength degradation is of paramount importance for maintaining the safety levels of RC structures affected by corrosion. In this regard, hybrid intelligence and machine learning techniques are proposed to build a new framework to predict the ultimate bond strength in between the corroded steel reinforcements and the surrounding concrete. The proposed computational techniques include the multilayer perceptron (MLP), the radial basis function neural network and the genetic expression programming methods. In addition to that, the Levenberg–Marquardt (LM) deterministic approach and two meta-heuristic optimization approaches, namely the artificial bee colony algorithm and the particle swarm optimization algorithm, are employed in order to guarantee an optimum selection of the hyper-parameters of the proposed techniques. The latter were implemented based on an experimental published database consisted of 218 experimental tests, which cover various factors related to the ultimate bond strength, such as compressive strength of the concrete, concrete cover, the type steel, steel bar diameter, length of the bond and the level of corrosion. Based on their performance evaluation through several statistical assessment tools, the proposed models were shown to predict the ultimate bond strength accurately; outperforming the existing hybrid artificial intelligence models developed based on the same collected database. More precisely, the MLP-LM model was, by far, the best model with a determination coefficient (R2) as high as 0.97 and 0.96 for the training and the overall data, respectively.