Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials
Achieving an optimal concrete mix design is critical for mechanical performance and sustainability, particularly by incorporating supplementary cementitious materials to promote eco-friendly concrete. This study introduces an intelligent concrete mix design method that optimizes performance and inte...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509524012324 |
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| author | Khuram Rashid Fatima Rafique Zunaira Naseem Fahad K. Alqahtani Idrees Zafar Minkwan Ju |
| author_facet | Khuram Rashid Fatima Rafique Zunaira Naseem Fahad K. Alqahtani Idrees Zafar Minkwan Ju |
| author_sort | Khuram Rashid |
| collection | DOAJ |
| description | Achieving an optimal concrete mix design is critical for mechanical performance and sustainability, particularly by incorporating supplementary cementitious materials to promote eco-friendly concrete. This study introduces an intelligent concrete mix design method that optimizes performance and integrates machine learning and multi-criteria decision-making techniques. In the initial phase, three machine learning models—Decision Tree, Random Forest, and Multi-layer Perceptron—were developed and trained on a dataset of 1030 records to predict sustainable concrete's compressive strength accurately. Among these models, the Random Forest model demonstrated the highest accuracy, exceeding 90 % in testing, affirming its superior predictive capability for concrete strength compared to other models reported in the literature. In Tthe optimization phase, multi-objective optimization was involved. Thus, compressive strength was optimized alongside sustainability criteria, including CO₂ emissions and cost-effectiveness. This optimization was processed by Pareto analysis and the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to identify the most effective mix design, which was achieved with a binary combination of supplementary cementitious materials. This combination was identified as the top-ranked mix regarding sustainability and performance metrics. For the validation in practical cases involving commercial buildings, it has acheived 27 % cost reduction and 63 % decrease in CO₂ emissions compared to conventional concrete mixing. This intelligent mix design approach significantly advances sustainable concrete development in reducing environmental impact as well as promoting cost-effective. |
| format | Article |
| id | doaj-art-b8218a3afd3542dc9b53565479c1a2ee |
| institution | DOAJ |
| issn | 2214-5095 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-b8218a3afd3542dc9b53565479c1a2ee2025-08-20T02:39:09ZengElsevierCase Studies in Construction Materials2214-50952024-12-0121e0408010.1016/j.cscm.2024.e04080Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materialsKhuram Rashid0Fatima Rafique1Zunaira Naseem2Fahad K. Alqahtani3Idrees Zafar4Minkwan Ju5Department of Architectural Engineering and Design, Faculty of Civil Engineering, University of Engineering and Technology, Lahore, Pakistan; Corresponding authors.Department of Architectural Engineering and Design, Faculty of Civil Engineering, University of Engineering and Technology, Lahore, Pakistan; Faculty of Engineering, University of Management and Technology, Lahore, PakistanSchool of Civil and Environmental Engineering, University of New South Wales, Kensington, NSW, AustraliaDepartment of Civil Engineering, College of Engineering, King Saud University, P.O.Box 800, Riyadh 11421, Saudi ArabiaDepartment of Cathodic Protection, Saith Limited, ICM House, Yeoman Road, Ringwood, Hampshire BH24 3FA, United KingdomDepartment of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea; Corresponding authors.Achieving an optimal concrete mix design is critical for mechanical performance and sustainability, particularly by incorporating supplementary cementitious materials to promote eco-friendly concrete. This study introduces an intelligent concrete mix design method that optimizes performance and integrates machine learning and multi-criteria decision-making techniques. In the initial phase, three machine learning models—Decision Tree, Random Forest, and Multi-layer Perceptron—were developed and trained on a dataset of 1030 records to predict sustainable concrete's compressive strength accurately. Among these models, the Random Forest model demonstrated the highest accuracy, exceeding 90 % in testing, affirming its superior predictive capability for concrete strength compared to other models reported in the literature. In Tthe optimization phase, multi-objective optimization was involved. Thus, compressive strength was optimized alongside sustainability criteria, including CO₂ emissions and cost-effectiveness. This optimization was processed by Pareto analysis and the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to identify the most effective mix design, which was achieved with a binary combination of supplementary cementitious materials. This combination was identified as the top-ranked mix regarding sustainability and performance metrics. For the validation in practical cases involving commercial buildings, it has acheived 27 % cost reduction and 63 % decrease in CO₂ emissions compared to conventional concrete mixing. This intelligent mix design approach significantly advances sustainable concrete development in reducing environmental impact as well as promoting cost-effective.http://www.sciencedirect.com/science/article/pii/S2214509524012324Machine learningSupplementary cementitious materialsMulti-objective OptimizationCompressive strengthEnviro-economic analysis |
| spellingShingle | Khuram Rashid Fatima Rafique Zunaira Naseem Fahad K. Alqahtani Idrees Zafar Minkwan Ju Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials Case Studies in Construction Materials Machine learning Supplementary cementitious materials Multi-objective Optimization Compressive strength Enviro-economic analysis |
| title | Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials |
| title_full | Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials |
| title_fullStr | Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials |
| title_full_unstemmed | Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials |
| title_short | Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials |
| title_sort | machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials |
| topic | Machine learning Supplementary cementitious materials Multi-objective Optimization Compressive strength Enviro-economic analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2214509524012324 |
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