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Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to ensure their safety and reliability. However, the main cables of suspension bridges inevitably deteriorate over time due to corrosion, as a result of their operational and environmental conditions. Thus, accurate annual corrosion rate predictions are crucial for maintaining reliable structures and optimal maintenance operations. However, the corrosion rate is a chaotic and complex phenomenon with highly nonlinear behavior. This paper proposes a novel predictive model for the estimation of the annual corrosion rate in the main cables of suspension bridges. This is a hybrid model based on the multilayer perceptron (MLP) technique optimized using marine predators algorithm (MPA). In addition, well-known metaheuristic approaches such as the genetic algorithm (GA) and particle swarm algorithm (PSO) are employed to optimize the MLP. In order to implement the proposed model, a comprehensive database composed of 309 sample tests on the annual corrosion rate from all around the world, including various factors related to the surrounding environmental properties, is utilized. In addition, several input combinations are proposed for investigating the trigger factors in modeling the annual corrosion rate. The performance of the proposed models is evaluated using various statistical and graphical criteria. The results of this study demonstrate that the proposed hybrid MLP-MPA model provides stable and accurate predictions, while it transcends the previously developed approaches for solving this problem. The effectiveness of the MLP-MPA model shows that it can be used for further studies on the reliability analysis of the main cables of suspension bridges.
Mohamed El Amine Ben Seghier; José A. F. O. Corriea; Jafar Jafari-Asl; Abdollah Malekjafarian; Vagelis Plevris; Nguyen-Thoi Trung. On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm. Neural Computing and Applications 2021, 1 -17.
AMA StyleMohamed El Amine Ben Seghier, José A. F. O. Corriea, Jafar Jafari-Asl, Abdollah Malekjafarian, Vagelis Plevris, Nguyen-Thoi Trung. On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm. Neural Computing and Applications. 2021; ():1-17.
Chicago/Turabian StyleMohamed El Amine Ben Seghier; José A. F. O. Corriea; Jafar Jafari-Asl; Abdollah Malekjafarian; Vagelis Plevris; Nguyen-Thoi Trung. 2021. "On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm." Neural Computing and Applications , no. : 1-17.
The estimation of the failure probability for complex systems is a crucial issue for sustainability. Reliability analysis methods are needed to be developed to provide accurate estimations of the safety levels for the complex systems and structures of today. In this paper, a novel hybrid framework for the reliability analysis of engineering systems and structures is extended to reduce the computational burden. The proposed hybrid framework is named as SVR–CFORM and consists of coupling two parts: the first is an enhanced first-order reliability method (FORM) using nonlinear conjugate map (CFORM); the second is an artificial intelligence technique called support vector regression (SVR). The conjugate FORM (CFORM) is adaptively formulated to improve the robustness of the original iterative FORM algorithm, whereas the SVR technique is used to enhance the efficiency of the reliability analysis by reducing the computational burden. The performance of the proposed SVR–CFORM formulation is compared in terms of efficiency and robustness with several FORM formulas (i.e. HL–RF, directional stability transformation method, conjugate HL–RF and finite step length) through different numerical/structural reliability examples. Results indicate that the proposed SVR–CFORM formulation is more accurate and efficient than other reliability methods. Based on the comparative analysis results, the SVR technique can highly reduce the computational costs and accurately model the response of complex performance functions, while the iterative CFORM formulation found to provide stable and robust reliability index results compared to the others reliability methods.
Behrooz Keshtegar; Mohamed El Amine Ben Seghier; Enrico Zio; José A.F.O. Correia; Shun-Peng Zhu; Nguyen-Thoi Trung. Novel efficient method for structural reliability analysis using hybrid nonlinear conjugate map-based support vector regression. Computer Methods in Applied Mechanics and Engineering 2021, 381, 113818 .
AMA StyleBehrooz Keshtegar, Mohamed El Amine Ben Seghier, Enrico Zio, José A.F.O. Correia, Shun-Peng Zhu, Nguyen-Thoi Trung. Novel efficient method for structural reliability analysis using hybrid nonlinear conjugate map-based support vector regression. Computer Methods in Applied Mechanics and Engineering. 2021; 381 ():113818.
Chicago/Turabian StyleBehrooz Keshtegar; Mohamed El Amine Ben Seghier; Enrico Zio; José A.F.O. Correia; Shun-Peng Zhu; Nguyen-Thoi Trung. 2021. "Novel efficient method for structural reliability analysis using hybrid nonlinear conjugate map-based support vector regression." Computer Methods in Applied Mechanics and Engineering 381, no. : 113818.
Reinforced concrete (RC) beams are basic elements used in the construction of various structures and infrastructural systems. When exposed to harsh environmental conditions, the integrity of RC beams could be compromised as a result of various deterioration mechanisms. One of the most common deterioration mechanisms is the formation of different types of corrosion in the steel reinforcements of the beams, which could impact the overall reliability of the beam. Existing classical reliability analysis methods have shown unstable results when used for the assessment of highly nonlinear problems, such as corroded RC beams. To that end, the main purpose of this paper is to explore the use of a structural reliability method for the multi-state assessment of corroded RC beams. To do so, an improved reliability method, namely the three-term conjugate map (TCM) based on the first order reliability method (FORM), is used. The application of the TCM method to identify the multi-state failure of RC beams is validated against various well-known structural reliability-based FORM formulations. The limit state function (LSF) for corroded RC beams is formulated in accordance with two corrosion types, namely uniform and pitting corrosion, and with consideration of brittle fracture due to the pit-to-crack transition probability. The time-dependent reliability analyses conducted in this study are also used to assess the influence of various parameters on the resulting failure probability of the corroded beams. The results show that the nominal bar diameter, corrosion initiation rate, and the external loads have an important influence on the safety of these structures. In addition, the proposed method is shown to outperform other reliability-based FORM formulations in predicting the level of reliability in RC beams.
Mohamed Ben Seghier; Behrooz Keshtegar; Hussam Mahmoud. Time-Dependent Reliability Analysis of Reinforced Concrete Beams Subjected to Uniform and Pitting Corrosion and Brittle Fracture. Materials 2021, 14, 1820 .
AMA StyleMohamed Ben Seghier, Behrooz Keshtegar, Hussam Mahmoud. Time-Dependent Reliability Analysis of Reinforced Concrete Beams Subjected to Uniform and Pitting Corrosion and Brittle Fracture. Materials. 2021; 14 (8):1820.
Chicago/Turabian StyleMohamed Ben Seghier; Behrooz Keshtegar; Hussam Mahmoud. 2021. "Time-Dependent Reliability Analysis of Reinforced Concrete Beams Subjected to Uniform and Pitting Corrosion and Brittle Fracture." Materials 14, no. 8: 1820.
In this work, the performance of reliability methods for the probabilistic analysis of local scour at a bridge pier is investigated. The reliability of bridge pier scour is one of the important issues for the risk assessment and safety evaluation of bridges. Typically, the depth prediction of bridge pier scour is estimated using deterministic equations, which do not consider the uncertainties related to scour parameters. To consider these uncertainties, a reliability analysis of bridge pier scour is required. In the recent years, a number of efficient reliability methods have been proposed for the reliability-based assessment of engineering problems based on simulation, such as Monte Carlo simulation (MCS), subset simulation (SS), importance sampling (IS), directional simulation (DS), and line sampling (LS). However, no general guideline recommending the most appropriate reliability method for the safety assessment of bridge pier scour has yet been proposed. For this purpose, we carried out a comparative study of the five efficient reliability methods so as to originate general guidelines for the probabilistic assessment of bridge pier scour. In addition, a sensitivity analysis was also carried out to find the effect of individual random variables on the reliability of bridge pier scour.
Jafar Jafari-Asl; Mohamed Ben Seghier; Sima Ohadi; You Dong; Vagelis Plevris. A Comparative Study on the Efficiency of Reliability Methods for the Probabilistic Analysis of Local Scour at a Bridge Pier in Clay-Sand-Mixed Sediments. Modelling 2021, 2, 63 -77.
AMA StyleJafar Jafari-Asl, Mohamed Ben Seghier, Sima Ohadi, You Dong, Vagelis Plevris. A Comparative Study on the Efficiency of Reliability Methods for the Probabilistic Analysis of Local Scour at a Bridge Pier in Clay-Sand-Mixed Sediments. Modelling. 2021; 2 (1):63-77.
Chicago/Turabian StyleJafar Jafari-Asl; Mohamed Ben Seghier; Sima Ohadi; You Dong; Vagelis Plevris. 2021. "A Comparative Study on the Efficiency of Reliability Methods for the Probabilistic Analysis of Local Scour at a Bridge Pier in Clay-Sand-Mixed Sediments." Modelling 2, no. 1: 63-77.
Axial compression capacity (ACC) is an important parameter for the concrete-filled steel tubular columns to measure the resistance of these fundamental elements, which used in the construction of several structures types. For this purpose, the Gene Expression Programing (GEP) is proposed in this paper as a new framework for the development of novel models with closed-form equations to describe the behavior of the axial compression capacity (ACC) for Square Concrete-Filled Steel Tubular (SCFST) columns. For an accurate ACC modeling, six novel predictive formulas based on the GEP-approach were proposed by incorporating different combinations of the input variables. These latter were obtained from a large dataset that includes 300 experimental tests with different ranges and varieties. Besides, the most known codes and empirical correlations for modeling the behavior of ACC for SCFST columns were reviewed, whereas the performance, accuracy, and efficiency of the proposed models and the excited codes and correlations were investigated and compared using several statistical criteria and graphical illustration. Results show that the best explicit closed-form correlation extracted based on the GEP-approach exhibit an overall coefficient of determination (R2) value of 0.9943. Furthermore, the outcome results indicate that the efficiency of the proposed GEP-based formulations outperform the excited codes and correlations, which proves that the GEP is a powerful technique to derive a new model for modeling the complex behavior of the ACC for SCFST columns.
Mohamed El Amine Ben Seghier; Xiao-Zhi Gao; Jafar Jafari-Asl; Duc-Kien Thai; Sima Ohadi; Nguyen-Thoi Trung. Modeling the nonlinear behavior of ACC for SCFST columns using experimental-data and a novel evolutionary-algorithm. Structures 2021, 30, 692 -709.
AMA StyleMohamed El Amine Ben Seghier, Xiao-Zhi Gao, Jafar Jafari-Asl, Duc-Kien Thai, Sima Ohadi, Nguyen-Thoi Trung. Modeling the nonlinear behavior of ACC for SCFST columns using experimental-data and a novel evolutionary-algorithm. Structures. 2021; 30 ():692-709.
Chicago/Turabian StyleMohamed El Amine Ben Seghier; Xiao-Zhi Gao; Jafar Jafari-Asl; Duc-Kien Thai; Sima Ohadi; Nguyen-Thoi Trung. 2021. "Modeling the nonlinear behavior of ACC for SCFST columns using experimental-data and a novel evolutionary-algorithm." Structures 30, no. : 692-709.
The main objective of this paper is to develop accurate novel frameworks for the estimation of the maximum pitting corrosion depth in oil and gas pipelines based on data-driven techniques. Thus, different advanced approaches using Artificial Intelligence (AI) models were applied, including Artificial Neural Network (ANN), M5 Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS), Locally Weighted Polynomials (LWP), Kriging (KR), and Extreme Learning Machines (ELM). Additionally, a total of 259 measurement samples of maximum pitting corrosion depth for pipelines located in different environments were extracted from the literature and used for developing the AI-models in terms of training and testing.Furthermore, an investigation was carried out on the relationship between the maximum pitting depths and several combinations of probable factors that induce the pitting growth process such as the pipeline age, and the surrounding environmental properties. The results of the proposed AI-frameworks were compared using various criteria. Thus, statistical, uncertainty and external validation analyses were utilized to compare the efficiency and accuracy of the proposed AI-models and to investigate the main contributing factors for accurate predictions of the maximum pitting depth in the oil and gas pipeline.
Mohamed El Amine Ben Seghier; Behrooze Keshtegar; Mohammed Taleb-Berrouane; Rouzbeh Abbassi; Nguyen-Thoi Trung. Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines. Process Safety and Environmental Protection 2021, 147, 818 -833.
AMA StyleMohamed El Amine Ben Seghier, Behrooze Keshtegar, Mohammed Taleb-Berrouane, Rouzbeh Abbassi, Nguyen-Thoi Trung. Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines. Process Safety and Environmental Protection. 2021; 147 ():818-833.
Chicago/Turabian StyleMohamed El Amine Ben Seghier; Behrooze Keshtegar; Mohammed Taleb-Berrouane; Rouzbeh Abbassi; Nguyen-Thoi Trung. 2021. "Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines." Process Safety and Environmental Protection 147, no. : 818-833.
Spillways are essential parts of dams, in which the main task of these structures is to allow the passing of excess water and floods from the upstream to the downstream. In this regards, the main goal of this paper is proposing a novel framework for the probabilistic design of labyrinth spillway structures using a new developed reliability-based design optimization (RBDO) approach. In this RBDO approach, the total volume of the spillway is considered as the objective function of the optimization problem under uncertainties, while the labyrinth spillway parameters are considered as the design variables. Hereafter, to solve the formulated RBDO problem of the labyrinth spillway design, a new proposed model that consist of coupling the Monte Carlo Simulation (MCS) with a hybrid Artificial Neural Network (ANN) based Whale Optimization Algorithm (WOA) model is developed. The hybrid ANN-WOA is utilized to approximate the labyrinth spillway response in order to reduce the computational cost during the RBDO analysis. The proposed MCS-ANN-WOA model was implemented on the Ute dam labyrinth spillway at Logan, New Mexico (USA). The obtained results showed that the proposed RBDO model performance is more accurate and robust compared to the deterministic optimization (DO) approaches for an optimal design of the labyrinth spillway shape with the consideration of the safety levels.
Jafar Jafari-Asl; Mohamed El Amine Ben Seghier; Sima Ohadi; Pieter van Gelder. Efficient method using Whale Optimization Algorithm for reliability-based design optimization of labyrinth spillway. Applied Soft Computing 2020, 101, 107036 .
AMA StyleJafar Jafari-Asl, Mohamed El Amine Ben Seghier, Sima Ohadi, Pieter van Gelder. Efficient method using Whale Optimization Algorithm for reliability-based design optimization of labyrinth spillway. Applied Soft Computing. 2020; 101 ():107036.
Chicago/Turabian StyleJafar Jafari-Asl; Mohamed El Amine Ben Seghier; Sima Ohadi; Pieter van Gelder. 2020. "Efficient method using Whale Optimization Algorithm for reliability-based design optimization of labyrinth spillway." Applied Soft Computing 101, no. : 107036.
Pipelines failures during its service life caused by the impact of multiple corrosion defects are most probable to occur, which may induce severe consequences as compared to single defect acting alone. Interaction rules were developed for multiple defects in the longitudinal and circumferential directions previously, but the knowledge on two or more defects aligned radially is still not well understood. This paper aims at investigating new interacting limits when corrosion defects are acting both on the internal and external wall, separated by a radial spacing, SR. The studies were carried out using burst test experiments as well as numerical modelling using pipe specimens of type API 5L X42. Six colonies of radial interacting corrosion defect arrangements were tested. The paper investigates the effects of the radial interaction by means of radial limit,SRLim. It was found that increasing the defect depths of internal and external defects would highly reduce the failure pressure, Pmultiple. Depending on the orientations of the multiple defects acting radially, the failure pressure may be less or more than the case of single defect, Psingle. Thus, the failure pressure ratio of Pmultiple/Psingle should be used as a measure, with any values larger than 0.99 depict no interaction taken place among those multiple defects. From the six colonies, only two colonies showed the significance of radial interactions. The paper then suggests that when radial interaction does not presence, multiple corrosion defects acting radially may then be treated as single defect.
Nurul Neesa Idris; Zahiraniza Mustaffa; Mohamed El Amine Ben Seghier; Nguyen-Thoi Trung. Burst capacity and development of interaction rules for pipelines considering radial interacting corrosion defects. Engineering Failure Analysis 2020, 121, 105124 .
AMA StyleNurul Neesa Idris, Zahiraniza Mustaffa, Mohamed El Amine Ben Seghier, Nguyen-Thoi Trung. Burst capacity and development of interaction rules for pipelines considering radial interacting corrosion defects. Engineering Failure Analysis. 2020; 121 ():105124.
Chicago/Turabian StyleNurul Neesa Idris; Zahiraniza Mustaffa; Mohamed El Amine Ben Seghier; Nguyen-Thoi Trung. 2020. "Burst capacity and development of interaction rules for pipelines considering radial interacting corrosion defects." Engineering Failure Analysis 121, no. : 105124.
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.
Mohamed El Amine Ben Seghier; Hocine Ouaer; Mohammed Abdelfetah Ghriga; Nait Amar Menad; Duc-Kien Thai. Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete. Neural Computing and Applications 2020, 33, 6905 -6920.
AMA StyleMohamed El Amine Ben Seghier, Hocine Ouaer, Mohammed Abdelfetah Ghriga, Nait Amar Menad, Duc-Kien Thai. Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete. Neural Computing and Applications. 2020; 33 (12):6905-6920.
Chicago/Turabian StyleMohamed El Amine Ben Seghier; Hocine Ouaer; Mohammed Abdelfetah Ghriga; Nait Amar Menad; Duc-Kien Thai. 2020. "Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete." Neural Computing and Applications 33, no. 12: 6905-6920.
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.
Abdelkader Guillal; Mohamed El Amine Ben Seghier; Abdelbaki Nourddine; José A.F.O. Correia; Zahiraniza Bt Mustaffa; Nguyen-Thoi Trung. Probabilistic investigation on the reliability assessment of mid- and high-strength pipelines under corrosion and fracture conditions. Engineering Failure Analysis 2020, 118, 104891 .
AMA StyleAbdelkader Guillal, Mohamed El Amine Ben Seghier, Abdelbaki Nourddine, José A.F.O. Correia, Zahiraniza Bt Mustaffa, Nguyen-Thoi Trung. Probabilistic investigation on the reliability assessment of mid- and high-strength pipelines under corrosion and fracture conditions. Engineering Failure Analysis. 2020; 118 ():104891.
Chicago/Turabian StyleAbdelkader Guillal; Mohamed El Amine Ben Seghier; Abdelbaki Nourddine; José A.F.O. Correia; Zahiraniza Bt Mustaffa; Nguyen-Thoi Trung. 2020. "Probabilistic investigation on the reliability assessment of mid- and high-strength pipelines under corrosion and fracture conditions." Engineering Failure Analysis 118, no. : 104891.
The aim of this study is to develop a new framework for the prediction of stress intensity factor (SIF) using newly developed hybrid artificial intelligence (AI) models. To do so, an adaptive neuro‐fuzzy inference system optimized by two meta‐heuristic algorithms as genetic algorithm (ANFIS‐GA) and particle swarm optimization (ANFIS‐PSO) is proposed. Moreover, a database composed of 150 SIF values obtained using the finite element method (FEM) calculations is used for training and validating the two proposed AI models. The efficiency and accuracy of the proposed AI models were investigated through several assessment criteria. Results showed the outperformance of the ANFIS‐PSO model for accurate prediction of SIF values with R2= 0.9913, root mean square error (RMSE) = 23.6 and mean absolute error (MAE) = 18.07, whereas both AI models indicate a robust performance in the presence of input variability. Overall, the performed study provides a hybrid AI framework that can serve as an efficient numerical tool for SIF prediction and analysis.
Mohamed El Amine Ben Seghier; Hermes Carvalho; Behrooz Keshtegar; José A. F. O. Correia; Filippo Berto. Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor. Fatigue & Fracture of Engineering Materials & Structures 2020, 43, 2653 -2667.
AMA StyleMohamed El Amine Ben Seghier, Hermes Carvalho, Behrooz Keshtegar, José A. F. O. Correia, Filippo Berto. Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor. Fatigue & Fracture of Engineering Materials & Structures. 2020; 43 (11):2653-2667.
Chicago/Turabian StyleMohamed El Amine Ben Seghier; Hermes Carvalho; Behrooz Keshtegar; José A. F. O. Correia; Filippo Berto. 2020. "Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor." Fatigue & Fracture of Engineering Materials & Structures 43, no. 11: 2653-2667.
Accurate prediction of axial compression capacity (ACC) of concrete-filled steel tubular (CFST) columns is an important issue to maintain the safety levels of related structures and avoiding failure consequences. This paper aims to develop a new framework for accurate estimation of the ACC for square concrete-filled steel tubular (SCFST) columns based on a novel hybrid artificial intelligence technique. Therefore, the radial basis function neural network (RBFNN) was used as a predictive model to solve this problem, whereas for optimum generalization and accurate prediction, a new optimization algorithm inspired by the firefly movement was proposed, namely the firefly algorithm (FFA). Besides that, other well-known optimization algorithms were used to compare the accuracy of the new-developed predictive model, namely Differential Evolution (DE) and Genetic algorithm (GA). In addition, a large database of 300 experimental tests was collected from the open published literature to train the new hybrid proposed models in terms of RBFNN-GA, RBFNN-DE, and RBFNN-FFA. Several comparative criteria were used to evaluate the robustness and accuracy of the new proposed model. The obtained performances were compared with the ones given from the artificial neural network (ANN) method based on the trial and error method. Results showed that the novel predictive model based on the hybrid RBFNN with FFA provides the highest efficiency and accuracy in terms of predictive estimations of the ACC for SCFST columns compared to ANN, whereas the novel RBFNN-FFA model enhances the prediction results by 28%, 37%, and 52% compared to RBFNN-GA, RBFNN-DE, and ANN, respectively.
Sy Hung Mai; Mohamed El Amine Ben Seghier; Phuong Lam Nguyen; Jafar Jafari-Asl; Duc-Kien Thai. A hybrid model for predicting the axial compression capacity of square concrete-filled steel tubular columns. Engineering with Computers 2020, 1 -18.
AMA StyleSy Hung Mai, Mohamed El Amine Ben Seghier, Phuong Lam Nguyen, Jafar Jafari-Asl, Duc-Kien Thai. A hybrid model for predicting the axial compression capacity of square concrete-filled steel tubular columns. Engineering with Computers. 2020; ():1-18.
Chicago/Turabian StyleSy Hung Mai; Mohamed El Amine Ben Seghier; Phuong Lam Nguyen; Jafar Jafari-Asl; Duc-Kien Thai. 2020. "A hybrid model for predicting the axial compression capacity of square concrete-filled steel tubular columns." Engineering with Computers , no. : 1-18.
In the present study, experimental and modeling investigations were performed and combined to implement trustworthy paradigms to predict the viscosity value under different circumstances and a wide variety of conditions. The experimental approach was conducted on a considerable number of Iranian crude samples using a Rolling Ball viscometer. Accordingly, more than 1000 experimental points were gained. These latter were utilized as a databank in the modeling approach which included many advanced soft computing techniques, namely radial basis function (RBF) neural network, multilayer perceptron (MLP), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), decision trees (DTs) and random forest (RF). When performing the modeling tasks using these techniques, two distinct cases were considered: the first includes all available parameters as inputs such as pressure, temperature, API°, Mw of C12+ and the mole fractions till C11; whereas in the second case, a grouping scheme was considered to reduce the number of fractions. The obtained results revealed that DTs for the first case is the best implemented model with an overall average absolute relative deviation (AARD) of 3.379%. In addition, the comparison results with the preexisting approaches showed the superiority of the newly proposed model.
Mohsen Talebkeikhah; Menad Nait Amar; Ali Naseri; Mohammad Humand; Abdolhossein Hemmati-Sarapardeh; Bahram Dabir; Mohamed El Amine Ben Seghier. Experimental measurement and compositional modeling of crude oil viscosity at reservoir conditions. Journal of the Taiwan Institute of Chemical Engineers 2020, 109, 35 -50.
AMA StyleMohsen Talebkeikhah, Menad Nait Amar, Ali Naseri, Mohammad Humand, Abdolhossein Hemmati-Sarapardeh, Bahram Dabir, Mohamed El Amine Ben Seghier. Experimental measurement and compositional modeling of crude oil viscosity at reservoir conditions. Journal of the Taiwan Institute of Chemical Engineers. 2020; 109 ():35-50.
Chicago/Turabian StyleMohsen Talebkeikhah; Menad Nait Amar; Ali Naseri; Mohammad Humand; Abdolhossein Hemmati-Sarapardeh; Bahram Dabir; Mohamed El Amine Ben Seghier. 2020. "Experimental measurement and compositional modeling of crude oil viscosity at reservoir conditions." Journal of the Taiwan Institute of Chemical Engineers 109, no. : 35-50.
The present work aims at applying Machine Learning approaches to predict CO2 viscosity at different thermodynamical conditions. Various data-driven techniques including multilayer perceptron (MLP), gene expression programming (GEP) and group method of data handling (GMDH) were implemented using 1124 experimental points covering temperature from 220 to 673 K and pressure from 0.1 to 7960 MPa. Viscosity was modelled as function of temperature and density measured at the stated conditions. Four backpropagation-based techniques were considered in the MLP training phase; Levenberg-Marquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG) and resilient backpropagation (RB). MLP-LM was the most fit of the proposed models with an overall root mean square error (RMSE) of 0.0012 mPa s and coefficient of determination (R2) of 0.9999. A comparison showed that our MLP-LM model outperformed the best preexisting Machine Learning CO2 viscosity models, and that our GEP correlation was superior to preexisting explicit correlations.
Menad Nait Amar; Mohammed Abdelfetah Ghriga; Hocine Ouaer; Mohamed El Amine Ben Seghier; Binh Thai Pham; Pål Østebø Andersen. Modeling viscosity of CO2 at high temperature and pressure conditions. Journal of Natural Gas Science and Engineering 2020, 77, 103271 .
AMA StyleMenad Nait Amar, Mohammed Abdelfetah Ghriga, Hocine Ouaer, Mohamed El Amine Ben Seghier, Binh Thai Pham, Pål Østebø Andersen. Modeling viscosity of CO2 at high temperature and pressure conditions. Journal of Natural Gas Science and Engineering. 2020; 77 ():103271.
Chicago/Turabian StyleMenad Nait Amar; Mohammed Abdelfetah Ghriga; Hocine Ouaer; Mohamed El Amine Ben Seghier; Binh Thai Pham; Pål Østebø Andersen. 2020. "Modeling viscosity of CO2 at high temperature and pressure conditions." Journal of Natural Gas Science and Engineering 77, no. : 103271.
Avoiding failures of corroded steel structures are critical in offshore oil and gas operations. An accurate prediction of maximum depth of pitting corrosion in oil and gas pipelines has significance importance, not only to prevent potential accidents in future but also to reduce the economic charges to both industry and owners. In the present paper, efficient hybrid intelligent model based on the feasibility of Support Vector Regression (SVR) has been developed to predict the maximum depth of pitting corrosion in oil and gas pipelines, whereas the performance of well-known meta-heuristic optimization techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Firefly Algorithm (FFA), are considered to select optimal SVR hyper-parameters. These nature-inspired algorithms are capable of presenting precise optimal predictions and therefore, hybrid models are developed to integrate SVR with GA, PSO, and FFA techniques. The performances of the proposed models are compared with the traditional SVR model where its hyper-parameters are attained through trial and error process on the one hand and empirical models on the other. The developed models have been applied to a large database of maximum pitting corrosion depth. Computational results indicate that hybrid SVR models are efficient tools, which are capable of conducting a more precise prediction of maximum pitting corrosion depth. Moreover, the results revealed that the SVR-FFA model outperformed all other models considered in this study. The developed SVR-FFA model could be adopted to support pipeline operators in the maintenance decision-making process of oil and gas facilities.
Mohamed El Amine Ben Seghier; Behrooz Keshtegar; Kong Fah Tee; Tarek Zayed; Rouzbeh Abbassi; Nguyen Thoi Trung. Prediction of maximum pitting corrosion depth in oil and gas pipelines. Engineering Failure Analysis 2020, 112, 104505 .
AMA StyleMohamed El Amine Ben Seghier, Behrooz Keshtegar, Kong Fah Tee, Tarek Zayed, Rouzbeh Abbassi, Nguyen Thoi Trung. Prediction of maximum pitting corrosion depth in oil and gas pipelines. Engineering Failure Analysis. 2020; 112 ():104505.
Chicago/Turabian StyleMohamed El Amine Ben Seghier; Behrooz Keshtegar; Kong Fah Tee; Tarek Zayed; Rouzbeh Abbassi; Nguyen Thoi Trung. 2020. "Prediction of maximum pitting corrosion depth in oil and gas pipelines." Engineering Failure Analysis 112, no. : 104505.
Epistemic uncertainties are critical for reliable design of corroded pipes made of high-strength grade steel. In this work, corrosion defects geometries and operating pressure are provided as the epistemic uncertainties in reliability analysis. A framework of an iterative approach-based bi-loop is presented for fuzzy reliability analysis (FRA) of corroded pipelines to evaluate the fuzzy reliability index-based various fuzzy-random variables (FRVs). In the inner loop, the conjugate first-order reliability method using adaptive finite-step size is applied for carried out the reliability analysis. The outer loop is structured based on the fuzzy analysis corresponding to a modified particle swarm optimization as an intelligent tool. The adaptive conjugate fine step size is dynamically computed to adjust the conjugate sensitivity vector in the reliability loop. The sufficient descent condition is satisfied based on three-term conjugate first-order reliability method. The performance function of corroded pipelines is defined based on average shear stress yield-based plastic flow theory, remaining strength factor, and operating pressure. Two applicable examples as corroded pipelines made from X100 high-strength steel are given to illustrate the effects of epistemic uncertainties under corrosion defects. Investigation the results has shown that modeling of epistemic uncertainty in the reliability analysis of high-grade steel pipelines could result more reasonable reliability indexes. In addition, results indicate that FRVs have significant influence on fuzzy reliability index calculations, especially corrosion defect depth and operating pressure (as FRVs). The sensitivity measure of FRA demonstrated that fuzzy reliability index of corroded X100 steel pipelines is more sensitive to the FRVs means.
Mansour Bagheri; Shun-Peng Zhu; Mohamed El Amine Ben Seghier; Behrooz Keshtegar; Nguyen-Thoi Trung. Hybrid intelligent method for fuzzy reliability analysis of corroded X100 steel pipelines. Engineering with Computers 2020, 1 -15.
AMA StyleMansour Bagheri, Shun-Peng Zhu, Mohamed El Amine Ben Seghier, Behrooz Keshtegar, Nguyen-Thoi Trung. Hybrid intelligent method for fuzzy reliability analysis of corroded X100 steel pipelines. Engineering with Computers. 2020; ():1-15.
Chicago/Turabian StyleMansour Bagheri; Shun-Peng Zhu; Mohamed El Amine Ben Seghier; Behrooz Keshtegar; Nguyen-Thoi Trung. 2020. "Hybrid intelligent method for fuzzy reliability analysis of corroded X100 steel pipelines." Engineering with Computers , no. : 1-15.
The stable convergence and efficiency of reliability-based design optimization (RBDO) using performance measure approach (PMA) are the major issue to develop the reliability methods based on modified chaos control (MCC), hybrid chaos control (HCC) and finite-step length adjustment (FSL). However, these methods may be inefficient for RBDO problems with convex and concave probabilistic constraints. In this paper, an adaptive modified chaos control (AMC) is proposed to provide the robust and efficient results in RBDO. The proposed AMC is adjusted using dynamical chaos control factor, which is extracted using sufficient descent condition for PMA. Using sufficient criterion, the proposed AMC is adaptively combined with advanced mean value (AMV) to improve the performance of PMA, named as hybrid adaptive modified chaos control (HAMC). Considering the robustness and efficiency, the proposed HAMC is compared with several existing reliability methods by three nonlinear structural/mathematical performance functions and two RBDO problems. The results indicate that the proposed HAMC with sufficient descent condition provides superior convergences in terms of both robustness and efficiency, compared to existing PMA methods using AMV, MCC, HCC and FSL.
Behrooz Keshtegar; Debiao Meng; Mohamed El Amine Ben Seghier; Mi Xiao; Nguyen-Thoi Trung; Dieu Tien Bui. A hybrid sufficient performance measure approach to improve robustness and efficiency of reliability-based design optimization. Engineering with Computers 2020, 1 -14.
AMA StyleBehrooz Keshtegar, Debiao Meng, Mohamed El Amine Ben Seghier, Mi Xiao, Nguyen-Thoi Trung, Dieu Tien Bui. A hybrid sufficient performance measure approach to improve robustness and efficiency of reliability-based design optimization. Engineering with Computers. 2020; ():1-14.
Chicago/Turabian StyleBehrooz Keshtegar; Debiao Meng; Mohamed El Amine Ben Seghier; Mi Xiao; Nguyen-Thoi Trung; Dieu Tien Bui. 2020. "A hybrid sufficient performance measure approach to improve robustness and efficiency of reliability-based design optimization." Engineering with Computers , no. : 1-14.
Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng–Robinson (PR) or Soave–Redlich–Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201.
Hocine Ouaer; Amir Hossein Hosseini; Menad Nait Amar; Mohamed El Amine Ben Seghier; Mohammed Abdelfetah Ghriga; Narjes Nabipour; Pål Østebø Andersen; Amir Mosavi; Shahaboddin Shamshirband. Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids. Applied Sciences 2019, 10, 304 .
AMA StyleHocine Ouaer, Amir Hossein Hosseini, Menad Nait Amar, Mohamed El Amine Ben Seghier, Mohammed Abdelfetah Ghriga, Narjes Nabipour, Pål Østebø Andersen, Amir Mosavi, Shahaboddin Shamshirband. Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids. Applied Sciences. 2019; 10 (1):304.
Chicago/Turabian StyleHocine Ouaer; Amir Hossein Hosseini; Menad Nait Amar; Mohamed El Amine Ben Seghier; Mohammed Abdelfetah Ghriga; Narjes Nabipour; Pål Østebø Andersen; Amir Mosavi; Shahaboddin Shamshirband. 2019. "Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids." Applied Sciences 10, no. 1: 304.
The burst pressure of oil and gas pipelines with corrosion defects is the major failure mode of these structures. Structural reliability analysis is normally conducted to evaluate the robust design-based safe levels of corroded pipelines using the probabilistic failure model. In the current work, the abilities for robustness and efficiency of first order reliability method (FORM) formulas are investigated for corroded mid-strength grade steel pipes. These methods are mainly enhance FORM algorithms-based steepest descent search direction as directional stability transformation method (DSTM), finite-step length (FSL), finite-step adaptive length (FAL) and conjugate steepest descent search direction as conjugate HL-RF (CHL-RF), conjugate finite-step length (CFSL) and a proposed adaptive conjugate finite step length (ACFSL). In proposed ACFSL algorithm, the FORM formula is adaptively enhanced using the dynamical conjugate search direction to adapt the new iterations for the burst pressure failure mode which is computed using a probabilistic combined by plastic flow theory-based average shear stress yield criterion and remaining stress factor-based semi-elliptical defects. Comparative results indicate that three algorithms e.g. FAL, CFSL and ACFSL are the perfect convergence performances for reliability analysis of corroded pipeline compared to other formulas, while ACFSL provides the superior performances in term of efficiency and robustness. The safety level of these structures is highly sensitive to the corrosion defect depths and operating pressure.
Behrooz Keshtegar; Mohamed El Amine Ben Seghier; Shun-Peng Zhu; Rouzbeh Abbassi; Nguyen-Thoi Trung. Reliability analysis of corroded pipelines: Novel adaptive conjugate first order reliability method. Journal of Loss Prevention in the Process Industries 2019, 62, 103986 .
AMA StyleBehrooz Keshtegar, Mohamed El Amine Ben Seghier, Shun-Peng Zhu, Rouzbeh Abbassi, Nguyen-Thoi Trung. Reliability analysis of corroded pipelines: Novel adaptive conjugate first order reliability method. Journal of Loss Prevention in the Process Industries. 2019; 62 ():103986.
Chicago/Turabian StyleBehrooz Keshtegar; Mohamed El Amine Ben Seghier; Shun-Peng Zhu; Rouzbeh Abbassi; Nguyen-Thoi Trung. 2019. "Reliability analysis of corroded pipelines: Novel adaptive conjugate first order reliability method." Journal of Loss Prevention in the Process Industries 62, no. : 103986.
In this paper, the failure probability of corroded pipelines made by X60 steel grade is evaluated. For this complex real engineering failure problem, the burst corroded performance function is developed using an M5Tree model based on calibration with real burst test database. In addition statistical analysis of ILI-report data is conducted for best modeling of corrosion defects geometries (i.e. defects length and depth) based on Anderson-Darling statistic where different PDFs (i.e. Normal, Lognormal, Frechet, Gumbel, Weibull) were tested. Moreover, the effect of defects geometries on the failure probability of the case-studies were investigated for various operating regimes. Then the influence of distributions on the reliability analysis were also illustrated. Results indicated that increases in defects depth are strongly reduced the safety levels of this problem, where miss-selection of defects distributions could lead to conservatives results.
Mohamed El Amine Ben Seghier; Behrooz Keshtegar; José A.F.O. Correia; Grzegorz Lesiuk; Abílio M.P. De Jesus. Reliability analysis based on hybrid algorithm of M5 model tree and Monte Carlo simulation for corroded pipelines: Case of study X60 Steel grade pipes. Engineering Failure Analysis 2019, 97, 793 -803.
AMA StyleMohamed El Amine Ben Seghier, Behrooz Keshtegar, José A.F.O. Correia, Grzegorz Lesiuk, Abílio M.P. De Jesus. Reliability analysis based on hybrid algorithm of M5 model tree and Monte Carlo simulation for corroded pipelines: Case of study X60 Steel grade pipes. Engineering Failure Analysis. 2019; 97 ():793-803.
Chicago/Turabian StyleMohamed El Amine Ben Seghier; Behrooz Keshtegar; José A.F.O. Correia; Grzegorz Lesiuk; Abílio M.P. De Jesus. 2019. "Reliability analysis based on hybrid algorithm of M5 model tree and Monte Carlo simulation for corroded pipelines: Case of study X60 Steel grade pipes." Engineering Failure Analysis 97, no. : 793-803.