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Cost–Performance Multi-Objective Optimization of Quaternary-Blended Cement Concrete

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Publicado en:Buildings vol. 15, no. 22 (2025), p. 4074-4107
Autor principal: Abbas, Yassir M
Otros Autores: Babiker Ammar, Elwakeel Abobakr, Khan, Mohammad Iqbal
Publicado:
MDPI AG
Materias:
Accuracy
Concrete
Artificial intelligence
Datasets
Algorithms
Multiple criterion
Optimization techniques
Mechanical properties
Cement
Multiple objective analysis
Sustainable materials
Objectives
Sorting algorithms
Pareto optimum
Efficiency
Business metrics
Portland cement
Concrete mixing
Genetic algorithms
Sustainable development
Variables
Pareto optimization
Mixtures
Portland cements
Optimization algorithms
Compressive strength
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2075-5309
DOI:10.3390/buildings15224074
Fuente:Engineering Database