Machine Learning in Construction Materials: Advances and Applications

Dear Colleagues,

The rapid development of machine learning (ML) and artificial intelligence (AI) has opened new opportunities for advancing research and practice in construction materials. Traditional experimental and numerical approaches, while fundamental, often face challenges in capturing complex multiscale behaviors, nonlinear responses and long-term performance evolution of materials under diverse environmental and loading conditions. In this context, data-driven methods provide powerful complementary tools for material characterization, modeling, prediction and optimization.

This Special Issue aims to present recent advances and emerging applications of machine learning in construction materials, covering both fundamental research and engineering practice. Topics of interest include, but are not limited to, ML-assisted prediction of mechanical properties and durability; intelligent analysis of experimental and monitoring data; image- and sensor-based material characterization; integration of machine learning with numerical modeling and multiscale simulations and data-driven approaches for material design, quality assessment and performance evaluation.

By bringing together contributions from materials science, computational mechanics and civil engineering, this Special Issue seeks to provide a comprehensive overview of state-of-the-art methodologies and to highlight future research directions for intelligent, data-informed construction materials.

Prof. Dr. Junxing Zheng
Dr. Peng Cao
Guest Editors

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