@article{JIANG2026, 
author = {Zhan JIANG and Le TENG and Xinyang TONG and Boyuan CHENG and Qiang YUAN and Wujian LONG and Weina MENG and Yi BAO},
title = {Artificial Intelligence for Sustainable Concrete: Methods, Challenges and Opportunities},
year = {2026},
journal = {Journal of the Chinese Ceramic Society},
volume = {54},
number = {3},
pages = {1016-1031},
keywords = {concrete, cyber-physical system, digital twin, explainable artificial intelligence, multi-objective optimization, trustworthy machine learning, waste valorization},
url = {https://www.sciopen.com/article/10.14062/j.issn.0454-5648.20250765},
doi = {10.14062/j.issn.0454-5648.20250765},
abstract = {Concrete is the most widely used construction material with an annual consumption of about 30 billion tons. However, the production of concrete creates significant environmental problems. Cement is a key binder in concrete, and its production requires burning limestone in high-temperature kilns, which causes approximately 8% of global carbon dioxide emissions. As urbanization and infrastructure construction continue to grow rapidly, the demand for cement and concrete is expected to be increased by 12%–23% by 2050. If we do not develop sustainable alternatives, this will affect a climate change. To address this issue, the industry is increasingly using supplementary cementitious materials, such as fly ash, slag, and calcined clay. In addition, using solid waste like mine tailings and demolition waste also becomes an important strategy. These methods can reduce the use of cement clinkers and keep waste out of landfills, providing both environmental and economic benefits. However, incorporating these diverse materials makes the design process much harder. Conventional design methods rely on experience and trial-and-error experiments. This process is time-consuming, consumes many resources, and cannot fully reflect the complex interactions between different materials. For instance, mixing multiple types of waste materials creates a high-dimensional design space that is hard to manage with simple, linear experiments.Artificial Intelligence (AI) technology offers an effective way to solve these challenges. Machine learning models can accurately predict key properties of concrete, such as fresh properties, mechanical strength, and durability, based on the historical experimental data. This review systematically summarizes AI techniques for sustainable concrete. The research moves from simple data mapping to more intelligent systems. Initially, some classic models like artificial neural networks and support vector machines are used. These models act as a map between the ingredients (inputs) and the performance (outputs). They can be used for capturing non-linear relationships that conventional equations cannot obtain. For instance, models like XGBoost and Random Forest can predict compressive strength and chloride resistance with a high accuracy, even when using relatively small datasets. These models are often used in a design loop, i.e., predicting performance and using optimization algorithms to adjust the mixture, balancing high strength with lower cost and reduced carbon emissions.However, conventional AI models have a major problem, i.e., they are often "black boxes". This means engineers cannot see how the model makes decisions, leading to a lack of trust in safety-critical projects. Also, these models depend heavily on data quality, and the existing datasets in concrete science are often small, inconsistent, or lack standard formatting. To fix this issue, the focus is shifting to advanced AI models. This review highlights three key improvements. First, data augmentation techniques, such as generative adversarial networks, are used to create synthetic data. This can solve the problem of small datasets via learning the patterns of real data and generating new, realistic samples. Second, explainable AI is used to open the "black box". Methods like SHAP analysis can show exactly how much each ingredient contributes to the final strength, making the results easier to understand and verify. Third, knowledge-guided learning combines data with physical laws. AI models can follow scientific principles via adding physical constraints (like diffusion laws) or using Knowledge Graphs, making them more reliable and scientifically correct compared to pure data-driven models.Summary and ProspectsThis review concludes that AI makes a great progress in predicting performance and optimizing mixtures, while there are still some challenges before it is widely used in real engineering. The main challenges include the lack of high-quality open datasets, the poor ability of models to work on new materials (generalization), and the need for better trust and interpretability. Future research needs to move from static predictions to autonomous systems.In the future, an AI-driven autonomous system can be used for sustainable concrete. This involves building a cyber-physical system. In this system, sensors collect real-time data during concrete production. AI models can analyze the data and automatically adjust the mixture proportions to ensure consistent quality. This allows for adaptive manufacturing, reducing waste and ensuring that the concrete meets the design requirements. Furthermore, Digital Twin technology can extend this to the entire life of the structure. A digital twin is a virtual copy of the physical infrastructure. It can update itself using sensor data to monitor the health of the structure and calculate its carbon footprint in real time. This can favor to make better decisions about maintenance and repair, extending the service life of the infrastructure.Finally, we need to focus on five priorities, i.e., creating open benchmark datasets for fair comparison; developing physics-informed AI that combines data with expert knowledge; advancing multi-objective optimization to balance performance, cost, and carbon; building cyber-physical systems and digital twins; and establishing standards and training programs. AI should not replace human experts. Instead, it should act as an intelligent assistant, helping engineers to design greener and more durable concrete. This human-AI collaboration is a key to achieving a carbon-neutral built environment in the future.}
}