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This study explores data-driven models using machine learning techniques to predict the compressive strength of 3D-printed fiber-reinforced concrete (3DP-FRC). The models, including Gene Expression Programming, Multi-Expression Programming, and Decision Tree, demonstrated high prediction accuracy, with the GEP model performing the best. Key factors influencing compressive strength were identified, and the models aim to reduce experimental workload in optimizing concrete mixes for 3D printing.

Author

Harish Panghal

Language

EN