Lap-slip model of rebar-to-concrete in RC/ECC/UHPC based on GA-BP neural network

Rebar lap splicing is a widely used method of reinforcement connection in precast concrete structures, and the lap-slip model serves as an important prerequisite for conducting detailed mechanical calculations of reinforced concrete lap splice members. Most traditional bond-slip mechanical models are semi-empirical and semi-theoretical formulas derived from experimental data; they do not consider the effect of reinforcement lap spacing, which prevents them from accurately reflecting the force transfer characteristics of lap reinforcement in concrete. In this paper, a Back Propagation (BP) neural network based on a Genetic Algorithm (GA) is used to establish a lap-slip model of reinforced concrete considering rebar spacing, which improves on the current bond-slip mechanical model. Several sets of rebar lap tests based on RC/ECC/UHPC were designed, and full-sample parameter sensitivity analyses were carried out using the lap length, the thickness of the concrete cover, the rebar spacing and the stirrup ratio as test parameters. The results showed that rebar lap spacing was an important influencing factor in the proposed lap-slip model, with a sensitivity coefficient reaching 0.471. The lap-slip model, based on the GA-BP neural network, was developed and compared with existing models; the predictive ability of the model was verified by the ten-fold crossover method. The generalizability of the GA-BP model was verified by randomly selecting non-training test data as well as lap test data of rebar in different types of concrete. The results indicated that the model exhibits high predictive and generalization abilities, with most correlation coefficients exceeding 0.95. The proposed model was compared with a mechanical model established using the traditional fitting formula method, and the results showed that the prediction accuracy of the lap-slip model established based on the GA-BP neural network is slightly lower than that of the mechanical model obtained using the fitting formula method, but it still offers high prediction accuracy, and the prediction results are more objective and realistic, which is of guiding significance for practical applications and related research.