Bond performance and constitutive model of novel NPR rebar–UHPC system under monotonic loading

Negative Poisson’s ratio (NPR) rebar, as a new type of rebar, overcomes the traditional challenge of increased strength leading to reduced ductility. These materials are highly suitable for integration with ultra-high performance concrete (UHPC), which addresses the issue of low ductility in components. However, research on the bonding between NPR rebar and UHPC is limited, which is detrimental to the engineering applications of UHPC. This study first examines the bond properties between NPR rebar and UHPC. Subsequently, the applicability of various existing bond characteristic values calculation models for NPR rebar and UHPC was examined, and a regression calculation model for NPR rebar-UHPC bond characteristic values was proposed. Accordingly, an artificial neural network (ANN) bond characteristic values calculation model for NPR rebar-UHPC was established. Finally, a three-stage bond-slip ( τ - s ) constitutive modeling was established for convenient application. The results showed that existing prediction models often underestimate τ cr and τ u between NPR bars and UHPC, while overestimate the s cr and s u between NPR bars and UHPC, showing that existing models fail to accurately calculate the bond stress and slip between NPR bars and UHPC. The ANN-based model that can accurately predict the bond characteristic values between NPR bars and UHPC is developed, with all ANN model R 2 values exceeding 0.90, and has a lower MAPE and RMSE compared to the regression model. The proposed τ - s constitutive model’s calculation curve matches the experimental curve well, with an average correlation coefficient of 0.942. This indicates that the proposed constitutive model can be used to predict the bond performance of NPR rebar-UHPC under monotonic loading.