Guest Editors
Dr. Mehran Khan
Email: khan.mehran@ucd.ie
Affiliation: School of Civil Engineering, University College Dublin, Belfield, Ireland
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Research Interests: low-carbon cement and concrete, supplementary cementitious materials, waste-derived binders, durability of cement-based materials, machine learning applications
Dr. Umar Hayat
Email: umar.hayat@connect.polyu.hk
Affiliation: Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong
Homepage:
Research Interests: nano-mechanics of cement hydration products, molecular dynamics simulation of c-s-h, AI applications in cement and concrete
Summary
Artificial intelligence (AI) and data-driven methods are rapidly transforming the design, characterization, and performance optimization of cement-based materials. With growing demands for improved durability, efficiency, and sustainability in construction, AI-enabled approaches offer powerful tools to accelerate material development, optimize mix designs, and predict long-term performance across multiple length scales.
This Special Issue aims to present state-of-the-art research on AI-driven innovations in cement and concrete science. Contributions are invited that integrate machine learning, deep learning, and hybrid data-physics-based approaches with experimental, numerical, and multi-scale modeling techniques. Topics of interest include AI-assisted mix proportioning, performance prediction, durability modeling, and intelligent analysis of microstructural and nano-scale data.
The issue also welcomes studies combining AI with advanced characterization methods and simulations, such as nano-mechanics, image-based analysis, and molecular dynamics simulations of calcium-silicate-hydrate (C-S-H), to provide fundamental insights into structure-property relationships. Emphasis is placed on robust methodologies, interpretability, and practical relevance to cement and concrete engineering.
This Special Issue seeks original research articles and comprehensive reviews that advance the understanding and application of AI-driven techniques in cement-based materials, contributing to smarter, more resilient, and next-generation construction materials.
Suggested Topics (but not limited to)
· Machine learning and deep learning for cement and concrete mix design
· AI-based prediction of mechanical, durability, and rheological properties
· Data-driven durability modeling and service-life prediction
· Hybrid physics-informed and data-driven modeling approaches
· AI-assisted analysis of microstructure and pore networks
· Image-based and multi-scale data analytics for cementitious materials
· Nano-scale characterization supported by AI techniques
· Molecular dynamics simulations of C-S-H integrated with data-driven methods
· Intelligent optimization and decision-support tools for cement-based materials
· Industrial case studies and practical AI applications in cement and concrete engineering
Keywords
AI-driven cement, machine learning, deep learning, cement-based materials, durability prediction, data-driven modeling, calcium silicate hydrate (C-S-H), molecular dynamics simulation, intelligent concrete, smart construction materials
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