Improved Meta-learning Neural Network for the Prediction of the Historical Reinforced Concrete Bond–Slip Model Using Few Test Specimens

Abstract

The bond–slip model plays an important role in the structural analysis of reinforced concrete structures. However, many factors affect the bond–slip behavior, which means that a large number of tests are required to establish an accurate bond–slip model. This paper aims to establish a data-driven method for the prediction of the bond–slip model of historical reinforced concrete with few test specimens and many features. Therefore, a new Mahalanobis-Meta-learning Net algorithm was proposed, which can be used to solve the implicit regression problem in few-shot learning. Compared with the existing algorithms, the Mahalanobis-Meta-learning Net achieves fast convergence, accurate prediction and good generalization without performing a large number of tests. The algorithm was applied to the prediction task of the bond–slip model of square rebar-reinforced concrete. First, the first large pretraining database for the bond–slip model, BondSlipNet, was established containing 558 samples from the existing literature. The BondSlipNet database can be used to provide a priori knowledge for learning. Then, another database, named SRRC-Net, was obtained by 16 groups of pull-out tests with square rebar. The SRRC-Net database can be used to provide the posteriori knowledge. Finally, based on the databases, the algorithm not only successfully predicted the bond–slip model of square rebar-reinforced concrete, but also that of the other 23 types of reinforced concrete. The research results can provide a scientific basis for the conservation of square rebar-reinforced concrete structures and can contribute to the bond–slip model prediction of the other types of reinforced concrete structures.