Bond performance and strain evolution of rebar-glass sand concrete based on distributed fiber optic sensors and FreqDeformNet

Purpose This study aims to explore the impacts of waste glass sand (WGS) replacement ratios and bond lengths on the bond performance and interfacial bond strain evolution of rebar-glass sand concrete, addressing the lack of accurate strain prediction in existing research to advance the engineering application of this material. Design/methodology/approach Specimens with WGS replacement ratios (0%, 10%, 30% and 50%) and bond lengths (3d = 36mm, 5d = 60mm) were tested. Distributed fiber optic sensors (DFOS) enabled high-precision strain monitoring, and a Time-Frequency Deformable Fusion Neural Network (FreqDeformNet) was developed to analyze nonlinear strain sequences. Findings The results indicate that under a bond length of 3 times the rebar diameter, a 30% glass sand replacement ratio and under a bond length of 5 times the rebar diameter, a 10% glass sand replacement ratio, exhibit the optimal bond performance. Compared to the 3d bond length, the strain variation in the 5d bond length demonstrates a more pronounced shear lag phenomenon. FreqDeformNet outperforms traditional models, with the mean absolute error reduced by 17.4%, 92% and 77%, respectively, and the highest average coefficient of determination. Originality/value The integration of DFOS and FreqDeformNet can reveal the bond performance of rebar-glass sand concrete under different conditions, address the issues of cross-variable dynamic interaction and long-term dependencies in strain data and provide a high-precision prediction method for bond strain.