Optimizing Carbon Footprint and Strength in High-Performance Concrete Through Data-Driven Modeling

Saloua Helali, Shadiah Albalawi, Maer Alanazi, Bashayr Alanazi and Nizar Bel Hadj Ali ()
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Saloua Helali: Research and Technology Center of Energy, Technoparc Borj Cedria, Hammam Lif BP 095, Tunisia
Shadiah Albalawi: Department of Physics, Faculty of Science, University of Tabuk, King Faisal Road, Tabuk 47512, Saudi Arabia
Maer Alanazi: Department of Physics, Faculty of Science, University of Tabuk, King Faisal Road, Tabuk 47512, Saudi Arabia
Bashayr Alanazi: Department of Physics, College of Science, Northern Border University, Arar 73213, Saudi Arabia
Nizar Bel Hadj Ali: Modeling in Civil Engineering and Environment (MCEE), National School of Engineers, University of Gabes, Street Omar Elkhattab, Zrig, Gabes 6029, Tunisia

Sustainability, 2025, vol. 17, issue 17, 1-17

Abstract: High-performance concrete (HPC) is an essential construction material used for modern buildings and infrastructure assets, recognized for its exceptional strength, durability, and performance under harsh situations. Nonetheless, the HPC production process frequently correlates with elevated carbon emissions, principally attributable to the high quantity of cement utilized, which significantly influences its carbon footprint. In this study, data-driven modeling and optimization strategies are employed to minimize the carbon footprint of high-performance concretes while keeping their performance properties. Starting from an experimental dataset, artificial neural networks (ANNs), ensemble techniques (ETs), and Gaussian process regression (GPR) are employed to yield predictive models for compressive strength of HPC mixes. The model’s input variables are the various components of HPC: cement, water, superplasticizer, fly ash, blast furnace slag, and coarse and fine aggregates. Models are trained using a dataset of 356 records. Results proved that the GPR-based model exhibits excellent accuracy with a determination coefficient of 0.90. The prediction model is used in a double objective optimization task formulated to identify mix configurations that allow for high mechanical performance aligned with a reduced carbon emission. The multi-objective optimization task is undertaken using genetic algorithms (GAs). Promising results are obtained when the machine learning prediction model is associated with GA optimization to identify strong yet sustainable mix configurations.

Keywords: carbon footprint; high-performance concrete; machine learning; compressive strength; genetic algorithms (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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