Publications Scientifiques

Permanent URI for this communityhttps://dspace.univ-boumerdes.dz/handle/123456789/10

Browse

Author: search.filters.author.Ouaer, HocineHas files: No

Search Results

Now showing 1 - 5 of 5
  • No Thumbnail Available
    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, Mahdi
    Liquefaction 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 case
  • No Thumbnail Available
    Item
    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 Mohamed
    Rock 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 Em
  • No Thumbnail Available
    Item
    Robust smart schemes for modeling carbon dioxide uptake in metal - organic frameworks
    (Elsevier, 2021) Nait Amar, Menad; Ouaer, Hocine; Abdelfetah Ghriga, Mohammed
    The 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 temperature
  • No Thumbnail Available
    Item
    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, Hocine
    Background: - 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 Engineers
  • No Thumbnail Available
    Item
    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, Hocine
    Ionic 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 temperature