Guest Editor(s)
Dr. Xingquan (Jasmine) Wang
Email: xingquan.wang@sydney.edu.au
Affiliation: School of Civil Engineering, The University of Sydney, Sydney, Darlington NSW, Australia
Homepage:
Research Interests: Al-Enabled Sustainable Construction Materials,
Al-guided design and optimisation of low-carbon and carbon-negative construction materials,
Computational modelling and data-driven prediction of material properties and performance,
Al-assisted design and experimental testing of advanced composites and cementitious materials
Prof. Xiaohong Zhu
Email: xiaohong.zhu@bjut.edu.cn
Affiliation: State Key Laboratory of Bridge Safety and Resilience, Beijing University of Technology, Beijing, China
Homepage:
Research Interests: low-carbon concrete and multi-scale characterisation
Prof. Bochao Sun
Email: sunbochao@zju.edu.cn
Affiliation: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China
Homepage:
Research Interests: artificial intelligence for cementitious materials and concrete: multiscale, physics-informed, and explainable modelling
Dr. Hubao A
Email: a_hubao@whu.edu.cn
Affiliation: School of Urban Construction, Wuhan University of Science and Technology, Wuhan, China
Homepage:
Research Interests: multi-scale modelling of cement-based materials, interfacial mechanics and physics
Prof. Shuang Lu
Email: lus@hit.edu.cn
Affiliation: Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China
Homepage:
Research Interests: solid waste resource utilization for construction materials
Summary
Artificial intelligence is rapidly transforming cement and concrete research by providing new approaches to extract information from complex experimental data, understand multiscale material behavior, and accelerate materials design. Beyond conventional data-driven prediction, emerging AI methods are increasingly being integrated with physical principles, advanced characterization, and atomistic-to-macroscopic modeling, creating new opportunities to investigate hydration, microstructure evolution, mechanical properties, durability, and low-carbon cementitious materials.
This Special Issue aims to highlight recent advances in AI-assisted research for cement, concrete, and related building materials, with particular emphasis on approaches that deepen the understanding of material mechanisms and bridge experiments, modeling, and performance prediction. Contributions combining AI with experimental characterization, physics-based models, molecular simulations, and multiscale analysis are particularly encouraged.
Suggested themes include:
· AI-assisted microstructure characterization and reconstruction;
· machine learning for material properties and durability;
· physics-informed neural networks and uncertainty quantification;
· AI-enhanced hydration and microstructure modeling;
· AI-accelerated molecular dynamics and atomistic simulations;
· multiscale modeling linking chemistry, microstructure, and macroscopic performance;
· interpretable and explainable AI for cementitious materials;
· AI-guided design and optimization of low-carbon cement and concrete.
Keywords
artificial intelligence, cementitious materials, concrete,multiscale modeling, microstructure characterization, physics-informed neural networks, uncertainty quantification, molecular dynamics, materials design
Login
Register
Submit a Paper
Propose a Special lssue