Browsing by Author "Ghernaout, Djamel"

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AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction
(Elsevier, 2025) Sahraoui, Mohamed; Laouissi, Aissa; Karmi, Yacine; Hammoudi, Abderazek; Hani, Mostefa; Chetbani, Yazid; Belaadi, Ahmed; Alshaikh, Ibrahim M.H.; Ghernaout, Djamel
This research investigates the application of machine learning (ML) and metaheuristic optimization to improve the mechanical properties of sisal fiber-reinforced foamed concrete. A Deep Neural Network (DNN) was developed, optimized using the Grey Wolf Optimizer (GWO) and the Slime Mould Algorithm (SMA), to predict and optimize material properties. Thirty-four data points obtained through experimentation were utilized to train, test, and validate various machine learning models for the prediction of tensile strength (TS). Six predictive models were assessed for accuracy and generalization: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Linear Model (LM), Dragonfly Algorithm-based Deep Neural Network (DNN-DA), and Improved Grey Wolf Optimizer-based Deep Neural Network (DNN-IGWO). DNN-IGWO demonstrated enhanced predictive performance, significantly exceeding that of traditional machine learning models. The optimization process directed by the metaheuristic approach utilizing SMA demonstrated swift convergence in 216 iterations, identifying the optimal mix proportions of cement, water-to-cement ratio, sand, and sisal fiber content. The optimized composition achieved a tensile strength of 4.16 MPa, representing a 9.5 % enhancement compared to conventional experimental methods, which yielded 3.8 MPa. Statistical validation demonstrated the model's stability and reliability, evidenced by a notably low standard deviation (SD = 2.15 × 10⁻⁵), indicating minimal variability in predictions. This study illustrates a comprehensive AI-based framework for the optimization of cementitious materials, effectively integrating experimental and computational methodologies. The proposed approach minimizes dependence on labor-intensive trial-and-error testing and enhances the sustainability of construction materials by utilizing sisal fibers. The results emphasize the capabilities of metaheuristic-enhanced deep learning models in optimizing high-performance, environmentally friendly concrete mix design, thereby facilitating future advancements in intelligent material formulations for sustainable infrastructure.
Optimised heat exchange in a magnetised nanofluid-filled cavity using hybrid deep neural network and metaheuristic algorithms
(Taylor and Francis Ltd., 2025) Benderradji, Razik; Laouissi, Aissa; Karmi, Yacine; Abderazek, Hammoudi; Chetbani, Yazid; Belaadi, Ahmed; Mukalazi, Herbert; Ghernaout, Djamel; Chamkha, Ali
This study presents a comprehensive numerical investigation into steady-state mixed convection heat transfer within a square ventilated cavity containing a centrally positioned isothermal cold cylinder. The objective is to assess the combined effects of nanofluids and magnetic fields on thermal performance. The working fluids considered include pure water and water-based nanofluids enhanced with copper (Cu) and aluminium oxide (Al2O3) nanoparticles. Simulations were conducted across a range of Richardson numbers (0.1 < Ri < 100), Hartmann numbers (0 < Ha < 100), and nanoparticle volume fractions (0% < φ < 8%), using the finite volume method and the SIMPLER algorithm. Distinct from prior studies, this work bridges two gaps: (i) quantifying how high magnetic fields (Ha > 50) diminish nanoparticle-enhanced heat transfer and (ii) integrating artificial intelligence not only for prediction but also optimisation. Specifically, three machine learning models Decision Tree (DT), K-Nearest Neighbors (KNN), and a Deep Neural Network optimised via Genetic Algorithm (DNN-GA) were trained on 160 high-fidelity simulation datasets to estimate the average Nusselt number. Results demonstrated the DNN-GA’s superior accuracy (R² = 0.999, RMSE = 0.021) over DT (R² = 0.978) and KNN (R² = 0.921). Furthermore, five metaheuristic algorithms Queuing Search Algorithm (QSA), Barnacles Mating Optimiser (BMO), Search and Rescue (SAR), Gradient-Based Optimiser (GBO), and Manta Ray Foraging Optimisation (MRFO) were applied to maximise heat transfer. Optimisation identified Cu nanoparticles at Ri = 109.7, Ha = 9.0, and φ = 6% as optimal (Nu = 34.95), validated experimentally with 0.89% error. The findings confirm that increasing Ri and Ha enhances heat transfer efficiency (by 12–18%), while nanoparticle contribution declines (to 3–5%) at higher Ha. This work offers a dual contribution: advancing understanding of MHD nanofluid interactions in ventilated cavities and demonstrating a robust AI-driven framework for thermal system design. Highlights: Analysis of mixed convection in a ventilated cavity using Cu-water and Al2O3-water nanofluids under varying Richardson and Hartmann numbers. Examination of magnetic field impacts on heat transfer and nanofluid flow. Comparative study of Al2O3 and Cu nanoparticles on heat transfer enhancement. Provides valuable insights into the combined effects of nanoparticles, magnetic fields, and convection parameters. Machine learning models are very useful for predicting the Nusselt number. Metaheuristics algorithms are highly effective in optimising heat transfer processes
Pecan shells-based activated carbon for the removal of copper metal ions: optimization of the adsorption process using a full factorial design
(Taylor & Francis, 2024) Seffah, Karima; Lakehal, Imane; Ouriache, Hadjer; Sadoun, Louiza; Badis, Dalila; Khenchali, Karim; Bendhina, Abdellah; Nadji, Halima; Ghernaout, Djamel
This work investigates the removal of copper ions (Cu2+) from an aqueous solution by adsorption onto the surface of activated carbon (AC) produced from pecan shells. The research aims to identify a feasible and effective route for cleaning the wastewater from Cu2+. Chemical activation was carried out using sodium hydroxide. The AC physicochemical properties were characterised by scanning electron microscopy, Fourier-transform infrared, X-ray diffraction, and Brunauer-Emmett-Teller for measuring its specific surface area. To obtain a suitable removal of this metal ion, four physicochemical factors, including the contact time (120–360 min) the adsorbent dose (10–100 mg), initial concentration (10–50 mg/L), and pH (2–6) were optimised using the 24-full factorial design approach. A quadratic regression model representing the capacity of Cu2+ adsorption (Qe) was developed and validated by the analysis of variance. This approach was used to determine independent factors’ main and interaction effects on adsorption equilibrium capacity. The results consolidate similar studies showing that all the factors were significant, and the interactions among the factors were also significant. The optimum conditions for circumscribed fractional factorial design were adsorption time (120 min), adsorbent amount (10 mg), initial metal ions (50 mg/L), and pH 6. The pseudo-first-order model correctly describes the adsorption kinetics with Qe, reached 51.41 mg/g. Modeling adsorption isotherms showed that the Freundlich model adequately describes the adsorption process.