Cost–Performance Multi-Objective Optimization of Quaternary-Blended Cement Concrete
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| הוצא לאור ב: | Buildings vol. 15, no. 22 (2025), p. 4074-4107 |
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| מחבר ראשי: | |
| מחברים אחרים: | , , |
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MDPI AG
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| גישה מקוונת: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 022 | |a 2075-5309 | ||
| 024 | 7 | |a 10.3390/buildings15224074 |2 doi | |
| 035 | |a 3275506378 | ||
| 045 | 2 | |b d20251115 |b d20251130 | |
| 084 | |a 231437 |2 nlm | ||
| 100 | 1 | |a Abbas, Yassir M |u Department of Civil Engineering, College of Engineering, King Saud University, Riyadh 12372, Saudi Arabia | |
| 245 | 1 | |a Cost–Performance Multi-Objective Optimization of Quaternary-Blended Cement Concrete | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The development of sustainable concrete capable of trading off the mechanical performance and cost remains a persistent scientific and engineering challenge. Although previous research has employed multi-objective optimization for binary and ternary cement blends, the simultaneous optimization of quaternary-blended systems, incorporating multiple supplementary cementitious materials, has received little systematic attention. This study addresses this gap by introducing an interpretable artificial intelligence (AI)-driven approach that integrates the Category Boosting (CatBoost) algorithm with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to model and optimize the compressive strength (CS) and total cost of quaternary-blended concretes. A curated database of 810 experimentally documented mixtures was used to train and validate the model. CatBoost achieved superior predictive performance (R2 = 0.987, MAE = 1.574 MPa), while Shapley additive explanations identified curing age, water-to-binder ratio, and Portland cement content as the dominant parameters governing CS. Multi-objective optimization produced Pareto-optimal elite mixtures achieving CS of 51–80 MPa, with a representative 60 MPa mix requiring approximately 62% less cement than conventional designs. The findings establish a scientifically grounded, interpretable methodology for data-driven design of low-carbon, high-performance concretes and demonstrate, for the first time, the viability of AI-assisted multi-criteria optimization for complex quaternary-blended systems. This framework offers both methodological innovation and practical guidance for implementing sustainable construction materials. | |
| 653 | |a Accuracy | ||
| 653 | |a Concrete | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Datasets | ||
| 653 | |a Algorithms | ||
| 653 | |a Multiple criterion | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Mechanical properties | ||
| 653 | |a Cement | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Sustainable materials | ||
| 653 | |a Objectives | ||
| 653 | |a Sorting algorithms | ||
| 653 | |a Pareto optimum | ||
| 653 | |a Efficiency | ||
| 653 | |a Business metrics | ||
| 653 | |a Portland cement | ||
| 653 | |a Concrete mixing | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Sustainable development | ||
| 653 | |a Variables | ||
| 653 | |a Pareto optimization | ||
| 653 | |a Mixtures | ||
| 653 | |a Portland cements | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Compressive strength | ||
| 700 | 1 | |a Babiker Ammar |u School of Civil Engineering, College of Engineering, Sudan University of Science and Technology, Eastern Daim, Khartoum P.O. Box 72, Sudan | |
| 700 | 1 | |a Elwakeel Abobakr |u ALTEN UK, 3 Pride Pl, Derby DE24 8QR, UK; abobakr.elwakeel@alten.co.uk | |
| 700 | 1 | |a Khan, Mohammad Iqbal |u Department of Civil Engineering, College of Engineering, King Saud University, Riyadh 12372, Saudi Arabia | |
| 773 | 0 | |t Buildings |g vol. 15, no. 22 (2025), p. 4074-4107 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275506378/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275506378/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275506378/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |