AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction
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 (S...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025016317 |
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| author | Mohamed Sahraoui Aissa Laouissi Yacine Karmi Abderazek Hammoudi Mostefa Hani Yazid Chetbani Ahmed Belaadi Ibrahim M.H. Alshaikh Djamel Ghernaout |
| author_facet | Mohamed Sahraoui Aissa Laouissi Yacine Karmi Abderazek Hammoudi Mostefa Hani Yazid Chetbani Ahmed Belaadi Ibrahim M.H. Alshaikh Djamel Ghernaout |
| author_sort | Mohamed Sahraoui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3ae813f6df914dd1b4f49a5e0b6d8b5d |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-3ae813f6df914dd1b4f49a5e0b6d8b5d2025-08-20T03:25:08ZengElsevierResults in Engineering2590-12302025-06-012610556110.1016/j.rineng.2025.105561AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable constructionMohamed Sahraoui0Aissa Laouissi1Yacine Karmi2Abderazek Hammoudi3Mostefa Hani4Yazid Chetbani5Ahmed Belaadi6Ibrahim M.H. Alshaikh7Djamel Ghernaout8Institute of Architecture and Urbanism, Saad Dahlab Blida 1 University, AlgeriaDepartment of Mechanical Engineering, Faculty of Sciences and Technology, University of Mohamed El Bachir El Ibrahimi, Bordj Bou Arreridj, AlgeriaElectromechanical Department, Institute of Applied Sciences and Techniques, University of Constantine 1, Constantine, AlgeriaUR-MPE, M’hamed Bougara University, Independence Avenue, Boumerdes 35000, AlgeriaDepartment of Civil Engineering, Eskisehir Technical University, Eskisehir 26555, Türkiye; Laboratory of Mechanics and Materials Development, Department of Civil Engineering, Faculty of Science and Technology, University of Djelfa, P.O. Box 3117, Djelfa 17000, AlgeriaLaboratory of Mechanics and Materials Development, Department of Civil Engineering, Faculty of Science and Technology, University of Djelfa, P.O. Box 3117, Djelfa 17000, AlgeriaDepartment of Mechanical Engineering, Faculty of Technology, University 20 Août 1955-Skikda, El-Hadaiek Skikda, Algeria; Corresponding authors.Faculty of Engineering, Department of Civil Engineering, University of Science and Technology, Sana’a, Yemen; Corresponding authors.Chemical Engineering Department, College of Engineering, University of Ha’il, PO Box 2440, Ha’il 81441, Saudi Arabia; Chemical Engineering Department, Faculty of Engineering, University of Blida, PO Box 270, Blida 09000, AlgeriaThis 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.http://www.sciencedirect.com/science/article/pii/S2590123025016317Sisal fiber-reinforced concreteMachine learning (ML)Metaheuristic optimizationDeep neural networks (DNN)Tensile strength predictionSustainable construction |
| spellingShingle | Mohamed Sahraoui Aissa Laouissi Yacine Karmi Abderazek Hammoudi Mostefa Hani Yazid Chetbani Ahmed Belaadi Ibrahim M.H. Alshaikh Djamel Ghernaout AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction Results in Engineering Sisal fiber-reinforced concrete Machine learning (ML) Metaheuristic optimization Deep neural networks (DNN) Tensile strength prediction Sustainable construction |
| title | AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction |
| title_full | AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction |
| title_fullStr | AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction |
| title_full_unstemmed | AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction |
| title_short | AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction |
| title_sort | ai driven predicting and optimizing lignocellulosic sisal fiber reinforced lightweight foamed concrete a machine learning and metaheuristic approach for sustainable construction |
| topic | Sisal fiber-reinforced concrete Machine learning (ML) Metaheuristic optimization Deep neural networks (DNN) Tensile strength prediction Sustainable construction |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025016317 |
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