The Role of Artificial Intelligence in Optimization and Prediction of Cement Concrete Properties

The integration of artificial intelligence (AI) into concrete technology represents a paradigm shift
in construction materials engineering, enabling unprecedented capabilities in property prediction,
mix optimization, and quality control. This comprehensive literature review synthesizes recent
empirical and theoretical evidence on the application of AI techniques, including machine learning
algorithms, neural networks, metaheuristic optimization, response surface methodology, and
hybrid models, for predicting and optimizing the mechanical and durability properties of cement
concrete. The analysis reveals that AI-based models consistently outperform traditional regression
models in prediction accuracy, with hybrid approaches demonstrating superior performance
through the synergistic combination of multiple algorithms. Statistical design methods including
Scheffe’s simplex lattice, Box-Behnken design, and central composite design have enabled efficient
mix optimization with significantly reduced experimental runs. The integration of AI with waste
derived materials and supplementary cementitious materials has further advanced sustainable
concrete production, enabling precise prediction of performance characteristics of alternative
binders including geopolymers, recycled aggregates, and industrial by-products. However,
challenges persist including model interpretability, data quality requirements, generalization across
diverse material compositions, and computational complexity. This review identifies critical gaps
in real-time quality control applications, lifecycle prediction models, standardized benchmarking
frameworks, and multi-objective optimization approaches balancing performance, economics, and
sustainability, providing actionable recommendations for researchers, practitioners, and
policymakers.

Agunwamba, J., Okafor, F., & Tiza, M. T. (2024a). Application of Scheffe’s simplex lattice model
in concrete mixture design and performance enhancement. Environmental Research and
Technology, *7*(2), 270–279.
Agunwamba, J., Tiza, M. T., & Okafor, F. (2024b). An appraisal of statistical and probabilistic
models in highway pavements. Turkish Journal of Engineering, *8*(2), 300–329.
Agunwamba, J. C., Aho, M. I., Iorwua, M. B., & Tiza, M. T. (2016). Response surface optimization
of treated turbid water using banana stem juice. International Journal for Innovative Research in
Multidisciplinary Field, 2(8).
Agunwamba, J. C., & Tiza, M. T. (2023). An appraisal of mechanistic-empirical models (MEMs)
in pavement deterioration. Journal of Applied and Natural Sciences, Engineering and Technology, 3(2),
1–10.
Alwathaf, A., Abdel Jaber, M., & Hunaiti, Y. (2024). Enhancement and optimization of the
mechanical properties in cement concrete with recycled asphalt pavement (RAP). Buildings.
Anyanwu, C., Agunwamba, J. C., & Tiza, M. (2025). Artificial intelligence in removal of emerging
contaminants: A sustainable approach for environmental cleanup and circular economy.
In Applications of artificial intelligence in removal of emerging contaminants: A sustainable approach for
environmental clean-up and circular economy (Chapter 4). Elsevier. Retrieved from,
https://doi.org/10.1016/C2023-0-52926-X
Anyanwu, C., Agunwamba, J. C., & Tiza, M. (2025). Application of conventional methods and
artificial intelligence tools for wastewater treatment: Recent advancements and future
prospects. In R. T. Kapoor & M. Sillanpää (Eds.), Applications of artificial intelligence in removal of
emerging contaminants: A sustainable approach for environmental clean-up and circular economy (Chapter
5). Elsevier. Retrieved from, https://doi.org/10.1016/C2023-0-52926-X
Bheel, N., Shams, M. A., & Benjeddou, O. (2024). A comprehensive study on the impact of human
hair fiber and millet husk ash on concrete properties: response surface modeling and
optimization. Scientific Reports.
Deif, M. A., Attar, H., Alomoush, W., & Hafez, M. A. (2025). Optimizing concrete mix designs
with synthetic data generation and machine learning prediction models. Applied Computational
Intelligence and Soft Computing.
Dhawan, A., & Verma, M. (2025). Optimization of geopolymer concrete mix design using machine
learning for enhanced sulphate and chloride resistance. Asian Journal of Civil Engineering.
Farouk, A., Abdulrahman, S., Al-Osta, M., Malami, S., & Abba, S. (2025). Optimizing ultra-high
performance concrete with recycled fine and CO₂ reduction strategies: A machine learning
and swarm intelligence approach. Innovative Infrastructure Solutions, 10.
Fuqaha, S., & Zaki, A. (2025). Predicting bentonite plastic concrete performance using machine
learning. Journal of Applied Engineering and Technological Science.
Ganasen, N., Krishnaraj, L., Onyelowe, K., Alaneme, G. U., & Otu, O. N. (2023). Soft computing
techniques for predicting the properties of raw rice husk concrete bricks using regression
based machine learning approaches. Scientific Reports, 13.
Ganasen, N., Krishnaraj, L., Onyelowe, K., & Stephen, L. U. (2024). Machine learning
optimization of bio-sandcrete brick modelling using response surface methodology. Scientific
Reports, 14.
Gazi, M. U., Rupom, N. I., et al. (2025). Enhancing concrete performance: Machine learning
models for predicting and classifying tensile strength. European Conference on Cognitive
Ergonomics.
George, C., Sharath Chandra, S., Topalakatti, P. M., V, R., Patil, S., & Alaneme, G. U. (2026).
Machine learning driven optimization of biomedical waste ash concrete for sustainable
construction. PLoS ONE, 21(3).
Ghazy, M., Abd Elaty, M. A., & Abo-Elenain, M. T. (2021). Characteristics and optimization of
cement concrete mixes with recycled asphalt pavement aggregates. Innovative Infrastructure
Solutions.
Hafez, H., Teirelbar, A., Tošić, N., Ikumi, T., & de la Fuente, A. (2023). Data-driven optimization
tool for the functional, economic, and environmental properties of blended cement concrete
using supplementary cementitious materials. Journal of Building Engineering, 64, 105637.
Kaveh, A., Eskandari, A., & Piri, M. (2025). Metaheuristic-optimized machine learning for
mechanical property prediction in eco-friendly rubberized concrete. Periodica Polytechnica Civil
Engineering.
Khan, A. U., Asghar, R., & Shomurotova, S. (2024). Predictive modeling for compressive strength
of blended cement concrete using hybrid machine learning models. Multiscale and
Multidisciplinary Modeling, Experiments and Design.
Lee, K., & Wong, S. (2023). Multi-objective Taguchi optimization of cement concrete
incorporating recycled mixed plastic fine aggregate using modified Fuller’s equation. Buildings.
Maaze, M. R., Das, S. K., Garg, N., & Shrivastava, S. (2025). Economic-environmental and multi
criteria optimization for predicting alkaline ratios in waste cement concrete-based geopolymer
using central composite design. International Journal of Concrete Structures and Materials.
Mali, A., Kolhe, A., Gorde, P., & Solepatil, S. (2025). Deep learning enhanced framework for multi
objective optimization of cement-slag concrete for the balancing performance, economics,
and sustainability. Asian Journal of Civil Engineering.
Mandal, S., Shiuly, A., Sau, D., Mondal, A., & Sarkar, K. (2024). Study on the use of different
machine learning techniques for prediction of concrete properties from their mixture
proportions with their deterministic and robust optimisation. AI in Civil Engineering, 3.
Mei, X., Wang, Y., Zhu, X., & Liu, X. (2026). A machine learning-driven multi-objective
optimization model for enhancing the mechanical properties of silica fume-modified
magnesium phosphate cement concrete. Scientia Sinica Technologica.
Mendhe, V., Bahoria, B., et al. (2025). Multifunctional property predictions of nano-engineered
cementitious composites for high-performance concrete structures using hybrid machine
learning techniques. ENGINEERING Structure and Civil Engineering.
Mohammed, N. (2023). Characterization of sustainable concrete made from wastewater bottle caps
using a machine learning and RSM-CCD: towards performance and
optimization. AToMech1-2023 Supplement.
Nasr, D., Babagoli, R., & Khaghani Dehaghani, A. (2025). Enhancing sustainable concrete using
waste ceramic powder and natural pozzolan through experimental and machine learning
approaches. Scientific Reports, 15.
Naukhez, K., Vidya Sagar, R., & Kishen, J. (2025). Optimization of ultra-high performance
concrete with coarse aggregate for enhanced sustainability: a comprehensive assessment of
slump flow and mechanical properties. Journal of Sustainable Cement-Based Materials.
Ndarowa, T. E., Mahachi, J., & Ikotun, B. (2024). A review of optimization of limestone and
calcined clay cement (LC3) concrete mixtures for 3d printing. Proceedings of the International
Conference.
Nguyen, V. M., Nguyen Van, M., & Nguyen, V. (2023). Optimization of the engineering properties
of cement concrete containing gravel and waste rock using dense packing and response
surface methodology. Periodica Polytechnica Civil Engineering.
Nsobundu, E. J., & Tiza, M. T. (2025). A comprehensive evaluation of waste-derived materials for
sustainable construction practices. International Journal of Environmental Pollution and
Environmental Modelling, *8*(1), 26–42.
Okagbare, U. V., Salem, A. A., Tiza, M. T., & Tyongi, N. E. (2026). Sustainable concrete practices
for reducing environmental impact in the construction industry. Nnamdi Azikiwe University
Journal of Civil Engineering (NAUJCVE), 5(1), 92–98.
Onuzulike, C., Tiza, M. T., Jiya, V. H., Akande, E. O., & Ogunleye, E. (2023). Predicting crack
growth in a concrete building. Selcuk University Journal, *18*(1).
Shah, M. I., Memon, S., Niazi, M. D. K., Amin, M., Aslam, F., & Javed, M. (2021). Machine
learning-based modeling with optimization algorithm for predicting mechanical properties of
sustainable concrete.
Siddiq, M. U., Anwar, M. K., Almansour, F. H., Qurashi, M. A., & Adeel, M. (2025). AI-driven
optimization of fly ash-based geopolymer concrete for sustainable high strength and CO2
reduction: An application of hybrid Taguchi–Grey–ANN approach. Buildings, *15*(12).
Singh, A., Yadav, P., Rai, S., & Singh, V. (2025b). Machine learning-assisted optimization of eco
friendly concrete paving blocks incorporating textile sludge and glass powder for sustainable
construction. Asian Journal of Civil Engineering.
Singh, S. K., Tiza, M. T., & Alasali, B. E. (2025a). Utilization of sustainable resources for energy
efficiency using smart technologies. In A. Gupta, A. Chadha, & S. Jabin (Eds.), Artificial
intelligence for environmental sustainability: Global perspectives and technological advances. AAP Apple
Academic Press.
Tiza, M. T., Agunwamba, J., Okafor, F., & Solomon, S. (2025a). Prediction and optimization of
compressive strength of cement concrete with Box-Behnken model. Journal of International
Environmental Application & Science, *20*, 56–69.
Tiza, M. T., Imoni, S., Akande, E. O., Mogbo, O., Jiya, V. H., & Onuzulike, C. (2024b).
Revolutionizing infrastructure development: Exploring cutting-edge advances in civil
engineering materials. Recent Progress in Materials, 6(3),
Tiza, M. T., Okafor, F., & Agunwamba, J. (2024a). Energy Dispersive X-Ray Fluorescence
(EDXRF) - Oxide composition analysis of coarse aggregates and reclaimed asphalt pavement
(RAP) as construction materials. Politeknik Dergisi, 1(1).
Tiza, M. T., Onuzulike, C. O., Jiya, V., Sahabo, M. M., & Sesugh, T. (2023c). An overview of the
applications of artificial intelligence in water engineering. International Journal of Engineering
Science and Application, 7(1).
Tiza, M. T., Toryila Michael, Kpur, G., Ogunleye, E., Sharma, S., Singh, S. K., & Likassa, D. M.
(2023a). The potency of functionalized nanomaterials for industrial applications. Materials
Today: Proceedings.
Tiza, T. M., Mogbo, O., Singh, S. K., Shaik, N., & Shettar, M. P. (2022). Bituminous pavement
sustainability improvement strategies. Energy Nexus, 6.
Utsev, T., Tiza, M. T., Ogunleye, E., Sesugh, T., Jiya, V. H., & Onuzulike, C. (2023b).
Nanotechnology and the construction industry. NanoEra Journal, 3(1).
Utsev, T., Tiza, M. T., Sani, H. A., & Sesugh, T. (2022). Sustainability in the civil engineering and
construction industry: A review. Journal of Sustainable Construction Materials and Technologies, 7(1),
30–40.