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