Multi-Target Machine Learning Model for Optimized and Sustainable Concrete
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".
Deadline for manuscript submissions: 20 October 2026 | Viewed by 572
Editors
Interests: machine learning; shape memory alloy; reinforced concrete; alkali-activated concrete seismic risk; retrofitting; soil–structure interaction
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; machine learning; concrete material; cementless concrete; retrofitting; reinforced concrete
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Concrete is the most widely used construction material globally, yet its conventional production is associated with high energy consumption, substantial carbon dioxide emissions, and intensive use of natural resources. In response, alternative and sustainable concrete materials such as alkali-activated materials, geopolymer concrete, and blended cementitious systems incorporating industrial by-products like fly ash, ground granulated blast-furnace slag, silica fume, and agricultural waste have gained increasing attention. These materials exhibit complex physicochemical interactions governed by precursor composition, activator chemistry, curing conditions, and mixture proportions, leading to highly nonlinear and interdependent mechanical, durable, and environmental performance characteristics. Traditional experimental and empirical approaches often struggle to capture these complex relationships efficiently, particularly when multiple performance criteria must be satisfied simultaneously. To address these challenges, machine learning (ML) techniques offer a powerful data-driven framework for modeling and optimizing sustainable concrete systems. By learning from experimental and simulated datasets, ML models can effectively capture nonlinear interactions between material constituents, processing parameters, and resulting performance indicators. Ensemble learning methods and stacking-based hybrid models are particularly well suited for this application due to their ability to improve predictive accuracy, robustness, and generalization by combining multiple base learners. Building on this framework, a multi-target ML approach enables the simultaneous prediction and optimization of multiple concrete performance indicators, such as compressive strength, tensile strength, elastic modulus, durability indices, workability, shrinkage, embodied carbon, and costs. Unlike single-target models, multi-target learning explicitly accounts for correlations and trade-offs among performance metrics, which are critical for sustainable material design. This Special Issue on an integrated multi-target ensemble learning framework supports the development of optimized and sustainable concrete mixtures by balancing performance, durability, and environmental impact, while significantly reducing experimental effort and accelerating material innovation.
Dr. Farzin Kazemi
Dr. Neda Asgarkhani
Guest Editors
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Keywords
- machine learning method
- sustainable concrete
- alkali-activated materials
- geopolymer concrete
- multi-target prediction
- concrete mix optimization
- cementless concrete material
- mechanical properties
- durability performance
- environmental impact
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