Publications Scientifiques
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Item Machine learning-based Shapley additive explanations approach for corroded pipeline failure mode identification(Elsevier Ltd, 2024) Ben Seghier, Mohamed El Amine; Mohamed, Osama Ahmed; Ouaer, HocineRapid failure mode identification of oil and gas pipelines can prevent catastrophic consequences, improve fast intervention and enhance the design safety of these critical systems. This paper proposes explainable-based machine learning models using to determine the failure mode of corroded pipelines as a function of geometric configurations, material properties, and corrosion defect details. To determine the best identification model, this study examined eight machine learning models, including Nave Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and Category Boosting, based on a comprehensive experimental database for steel pipelines with various corrosion/crack defect configurations. Furthermore, the Shapley additive explanations approach is utilized to rank the input variables for failure mode identification and explains the machine learning model predicting a specific failure mode for a given sample. In identifying the failure mode of corroded pipelines, the proposed Extreme Gradient Boosting model indicated the highest accuracy in term of performance evaluation compared to all other proposed models. In addition, the model-explanation findings show that the important parameter influencing the failure mechanism of corroded pipelines is the depth of corrosion defects followed by the pipeline wall thickness. The proposed framework is adaptable enough to allow further use of experimental results for having new insights.Item Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential(Springer, 2022) Mingxiang, Cai; Ouaer, Hocine; Ahmed Salih, Mohammed; Xiaoling, Chen; Menad Nait, Amar; Hasanipanah, MahdiLiquefaction has caused many catastrophes during earthquakes in the past. When an earthquake is occurring, saturated granular soils may be subjected to the liquefaction phenomenon that can result in significant hazards. Therefore, a valid and reliable prediction of soil liquefaction potential is of high importance, especially when designing civil engineering projects. This study developed the least squares support vector machine (LSSVM) and radial basis function neural network (RBFNN) in combination with the optimization algorithms, i.e., the grey wolves optimization (GWO), differential evolution (DE), and genetic algorithm (GA) to predict the soil liquefaction potential. Afterwards, statistical scores such as root mean square error were applied to evaluate the developed models. The computational results showed that the proposed RBFNN-GWO and LSSVM-GWO, with Coefficient of Determination (R2) = 1 and Root Mean Square Error (RMSE) = 0, produced better results than other models proposed previously in the literature for the prediction of the soil liquefaction potential. It is an efficient and effective alternative for the soil liquefaction potential prediction. Furthermore, the results of this study confirmed the effectiveness of the GWO algorithm in training the RBFNN and LSSVM models. According to sensitivity analysis results, the cyclic stress ratio was also found as the most effective parameter on the soil liquefaction in the studied caseItem Intelligent prediction of rock mass deformation modulus through three optimized cascaded forward neural network models(Springer, 2022) Hasanipanah, Mahdi; Jamei, Mehdi; Mohammed, Ahmed Salih; Amar, Menad Nait; Ouaer, Hocine; Khedher, Khaled MohamedRock mass deformation modulus (Em) is a key parameter that is needed to be determined when designing surface or underground rock engineering constructions. It is not easy to determine the deformability level of jointed rock mass at the laboratory; thus, researchers have suggested different in-situ test methods. Today, they are the best methods; though, they have their own problems: they are too costly and time-consuming. Addressing such difficulties, the present study offers three advanced and efficient machine-learning methods for the prediction of Em. The proposed models were based on three optimized cascaded forward neural network (CFNN) using the Levenberg–Marquardt algorithm (LMA), Bayesian regularization (BR), and scaled conjugate gradient (SCG). The performance of the proposed models was evaluated through statistical criteria including coefficient of determination (R2) and root mean square error (RMSE). The computational results showed that the developed CFNN-LMA model produced better results than other CFNN-SCG and CFNN-BR models in predicting the Em. In this regard, the R2 and RMSE values obtained from CFNN-LMA, CFNN-SCG, and CFNN-BR models were equal to (0.984 and 1.927), (0.945 and 2.717), and (0.904 and 3.635), respectively. In addition, a sensitivity analysis was performed through the relevancy factor and according to its results, the uniaxial compressive strength (UCS) was the most impacting parameters on EmItem Robust smart schemes for modeling carbon dioxide uptake in metal - organic frameworks(Elsevier, 2021) Nait Amar, Menad; Ouaer, Hocine; Abdelfetah Ghriga, MohammedThe emission of greenhouse gases such as carbon dioxide (CO2) is considered the most acute issue of the 21st century around the globe. Due to this fact, significant efforts have been made to develop rigorous techniques for reducing the amount of CO2 in the atmosphere. Adsorption of CO2 in metal–organic frameworks (MOFs) is one of the efficient technologies for mitigating the high levels of emitted CO2. The main aim of this study is to examine the aptitudes of four advanced intelligent models, including multilayer perceptron (MLP) optimized with Levenberg-Marquardt (MLP-LMA) and Bayesian Regularization (MLP-BR), extreme learning machine (ELM), and genetic programming (GP) in predicting CO2 uptake in MOFs. A sufficiently widespread source of data was used from literature, including more than 500 measurements of CO2 uptake in13 MOFs with various pressures at two temperature values. The results showed that the implemented intelligent paradigms provide accurate estimations of CO2 uptake in MOFs. Besides, error analyses and comparison of the prediction performance revealed that the MLP-LMA model outperformed the other intelligent models and the prior paradigms in the literature. Moreover, the MLP-LMA model yielded an overall coefficient of determination (R2) of 0.9998 and average absolute relative deviation (AARD) of 0.9205%. Finally, the trend analysis confirmed the high integrity of the MLP-LMA model in prognosticating CO2 uptake in MOFs, and its predictions overlapped perfectly the measured values with changes in pressure and temperatureItem Predicting solubility of nitrous oxide in ionic liquids using machine learning techniques and gene expression programming(Elsevier, 2021) Nait Amar, Menad; Ghriga, MMohammed Abdelfetah; Ben Seghier, Mohamed El Amine; Ouaer, HocineBackground: - Nitrous oxide (N2O), as a potent greenhouse gas, is increasingly becoming a major multidisciplinary concern in recent years. Therefore, the removal of N2O using powerful green solvents such as ionic liquids (ILs) has turned into an attractive way to reduce the amount of N2O in the atmosphere. Methods: -The aim of this study was to establish rigorous models that can predict the solubility of N2O in various ILs. To achieve this, three advanced soft-computing methods, viz. cascaded forward neural network (CFNN), radial basis function neural network (RBFNN), and gene expression programming (GEP) were trained and tested using comprehensive experimental measurements. Significant Findings: - The obtained results demonstrated that the newly implemented models can predict the solubility of N2O in ILs with high accuracy. Besides, it was found that the CFNN model optimized using Levenberg-Marquardt (LM) algorithm was the best predictive paradigm (R2=0.9994 and RMSE=0.0047). Lastly, the Leverage technique was carried out, and the statistical validity of the newly implemented model was documented as more than 96% of data were located in the applicability realm of this paradigm. © 2021 Taiwan Institute of Chemical EngineersItem Rheological study of concentrated dispersions. Application to the drilling fluid(Institute of Physics Publishing, 2018) Ouaer, Hocine; Gareche, Mourad; Allal, AhmedIn order to understand the rheological behavior of concentrated aqueous Algerian bentonite dispersions of drilling (sodium bentonite of Mostaganem "m'zila"), rheological tests were carried out. By varying the concentration of bentonite, flow tests have allowed to estimate the yield stress and apparent viscosity for each concentration and to see their influence on the rheological behavior of these dispersions. In addition dynamic tests (oscillatory) are used to define the linear region of our samples, the state of our fluid (elastic solid or viscous liquid) and understanding the mechanisms of structuring of the particles constituting the material. In parallel, other tests coupled with rheological measurements such as x-rays diffraction to know the mineralogical composition and granulometry to estimate the bentonite particle size.Item Rheological studies and optimization of Herschel–Bulkley parameters of an environmentally friendly drilling fluid using genetic algorithm(Springer, 2018) Ouaer, Hocine; Gareche, Mourad; Rooki, RezaThe Herschel–Bulkley rheological parameters of an environmentally friendly drilling fluid formulated based on an Algerianbentonite and two polymers—hydroxyethyl cellulose and polyethylene glycol—have been optimized using a genetic algorithm.The effect of hydroxyethyl cellulose, temperature, pH and sodium chloride (NaCl) on the three-parameter Herschel-Bulkleymodel was also studied. The genetic algorithm technique provided improved rheological parameter characterization compared tothe nonlinear regression, especially in the case of drilling fluids formulated with sodium chloride making it a better choice.Furthermore, the oscillatory test offered more reliable yield stress values. The rheological parameters were found to be verysensitive to different conditions. Yield stress and consistency index increased with increasing the hydroxyethyl cellulose con-centration, reaching maximum at a temperature of 65 °C and decreased with decreasing pH and also when adding sodiumchloride to the drilling fluid. The flow index changed inversely to yield stress and consistency index. The physical origins of thesechanges in rheological parameters were discussed and correlation between variation in rheological parameters and bentonitesuspension properties were concluded. Based on these results, it is recommended to use the proposed formulation of drilling fluidat high temperature and when the formation of alkaline pH is encountered due to the gelation mechanism and to select theoptimum concentration of NaCl to avoid degradation of the rheological parametersItem On the evaluation of solubility of hydrogen sulfide in ionic liquids using advanced committee machine intelligent systems(Elsevier, 2021) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ouaer, HocineIonic Liquids (ILs) are increasingly emerging as new innovating green solvents with great importance from academic, industrial, and environmental perspectives. This surge of interest in considering ILs in various applications is owed to their attractive properties. Involvements in the gas sweetening and the reduction of the amounts of sour and acid gasses are among the most promising applications of ILs. In this study, new advanced committee machine intelligent systems (CMIS) were introduced for predicting the solubility of hydrogen sulfide (H2S) in various ILs. The implemented CMIS models were gained by linking robust data-driven techniques, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath rigorous schemes using group method of data handling (GMDH) and genetic programming (GP). The proposed paradigms were developed using an extensive database encompassing 1243 measurements of H2S solubility in 33 ILs. The performed comprehensive error investigation revealed that the newly implemented paradigms yielded very satisfactory prediction performance. Besides, it was found that CMIS-GP provided more accurate estimations of H2S solubility in ILs compared with both the other intelligent models and the best-prior paradigms. In this regard, the developed CMIS-GP exhibited overall average absolute relative deviation (AARD) and coefficient of determination (R2) values of 2.3767% and 0.9990, respectively. Lastly, the trend analyses demonstrated that the tendencies of CMIS-GP predictions were in excellent accordance with the real variations of H2S solubility in ILs with respect to pressure and temperatureItem Modeling viscosity of CO 2 at high temperature and pressure conditions(Elsevier, 2020) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ouaer, Hocine; Ben Seghier, Mohamed El Amine; Thai Pham, BinhThe 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.Item Rigorous connectionist models to predict carbon dioxide solubility in various ionic liquids(MDPI AG, 2020) Ouaer, Hocine; hossein hosseini, amir; Nait Amar, Menad; Ben Seghier, Mohamed El Amine; Ghriga, Mohammed Abdelfetah; Nabipour, Narjes; Pål Østebø, Andersen; Mosavi, Amir; Shamshirband, ShahaboddinEstimating the solubility of carbon dioxide in ionic liquids, using reliable models, is ofparamount 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, namelymultilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubilityof carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and fourthermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimentaldata points derived from the literature including 13 ILs were used (80% of the points for training and20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) andBayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical andgraphical assessments were applied to check the credibility of the developed techniques. The resultswere 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 lowestroot 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