3066-280XISSN (Online)
Volume 5 , Issue 1 , PP: 104-125, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Omnia M. Osama 1 * , Marwa M. Eid 2 , El-Sayed M. El-Rabaie 3
Doi: https://doi.org/10.54216/MOR.050106
Modern infrastructure is supported by concrete, which, however, is one of the most significant sources of anthropogenic CO2 emissions on an industrial scale, mainly due to clinker manufacturing, energy-intensive processing, and the widespread use of virgin aggregates. Following the intensification of climate regulations and net-zero goals, the literature investigating the practical use of low-carbon binders, CO2-sequestering concrete, circular-material solutions, and sophisticated modelling applications has increased exponentially as a plausible approach to decarbonizing the cement and concrete value chain. This paper synthesizes recent developments in three interconnected domains: (i) material innovations, including CO2-carbonated concretes, recycled aggregate and recycled cement systems, LC3 and CSA-based binders, alkali-activated and geopolymer materials, and waste-derived supplementary cementitious components; (ii) data-driven and AI-based frameworks for predicting mechanical performance, durability, and embodied emissions, encompassing supervised learning, hybrid optimization (e.g., ANN–GA, PSO-, and gradient-boosted models), generative mix design, and uncertainty-aware forecasting; and (iii) process- and system-level strategies such as plant-scale operational optimization, carbon capture integration, electricity-based emission accounting, and national or regional emission scenario modelling. Throughout these threads, the review demonstrates that multi-objective optimization and machine learning can reduce embodied CO2 by significant margins while simultaneously achieving or exceeding traditional performance metrics. Alternative binders and circular solutions have the potential to reduce process emissions by 20-80% under the right conditions, and intelligent operational control can provide an immediate and low-capital benefit in additional mitigation. The remaining issues are data standardization, model transferability, interpretability, and the incorporation of technological innovations, along with policy, economic, and implementation limitations. It is based on these insights that the paper proposes a research and implementation agenda: material innovation is coupled with AI-enabled design, monitoring, and decision support to accelerate the shift toward sustainable, intelligent, and climate-resilient concrete infrastructure.
Low-Carbon Cementitious Materials , Machine Learning Optimization , CO2 Emissions Reduction , AI-Driven Concrete Design , Sustainable Construction Materials
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