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

Author(s): Chengwen Zhang, Qing Chun, Ao Sun, Yijie Lin & HaoYu Wang

Publication: IJCSM

Volume: 16

Issue:

Appears on pages(s):

Keywords: neural network, bond–slip model, meta-learning, architectural heritage, square rebar

DOI: 10.1186/s40069-022-00530-y

Date: 9/30/2022

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.