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Author

Amir Fam

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#reinforcement learning Dataset Open access Sep 2026

Experimental Push-Off Test Dataset for Shear Transfer Capacity in GFRP-Reinforced Concrete Interfaces

This dataset compiles experimental push-off test results for evaluating the shear transfer capacity of concrete interfaces reinforced with Glass Fiber-Reinforced Polymer (GFRP) reinforcement. The database includes geometric, material, and reinforcement-related parameters used to characterize the tested specimens, including interface shear area, maximum aggregate size, reinforcement ratio and configuration, GFRP bar diameter, tensile strength and elastic modulus, concrete compressive strength, and experimentally measured shear transfer capacity. The dataset was assembled from published experimental studies and was used for the development and evaluation of machine-learning and regression-based predictive models for GFRP-reinforced concrete interfaces. It accompanies the study “Data-Driven Prediction of Shear Transfer Capacity in GFRP-Reinforced Concrete Interfaces” and supports reproducibility, model development, comparative assessment, and future research on shear transfer behavior of GFRP-reinforced concrete interfaces.

Hosein Naderpour, Elaine Marques Silva, Amir Fam · 0 citations
#reinforcement learning Dataset Open access Sep 2026

Experimental Push-Off Test Dataset for Shear Transfer Capacity in GFRP-Reinforced Concrete Interfaces

This dataset compiles experimental push-off test results for evaluating the shear transfer capacity of concrete interfaces reinforced with Glass Fiber-Reinforced Polymer (GFRP) reinforcement. The database includes geometric, material, and reinforcement-related parameters used to characterize the tested specimens, including interface shear area, maximum aggregate size, reinforcement ratio and configuration, GFRP bar diameter, tensile strength and elastic modulus, concrete compressive strength, and experimentally measured shear transfer capacity. The dataset was assembled from published experimental studies and was used for the development and evaluation of machine-learning and regression-based predictive models for GFRP-reinforced concrete interfaces. It accompanies the study “Data-Driven Prediction of Shear Transfer Capacity in GFRP-Reinforced Concrete Interfaces” and supports reproducibility, model development, comparative assessment, and future research on shear transfer behavior of GFRP-reinforced concrete interfaces.

Hosein Naderpour, Elaine Marques Silva, Amir Fam · 0 citations