Modeling of Steel-Reinforced Grout Composite System-to-Concrete Bond Capacity Using Artificial Neural Networks

Abstract

The use of externally bonded composite systems is recognized as an effective solution for strengthening existing reinforced concrete (RC) structures. Steel-reinforced grout (SRG) is an attractive option, because of its compatibility with the concrete substrate and mechanical properties. However, a critical aspect is the delamination that might affect the steel textile–mortar and the mortar–concrete substrate interfaces. An experimental and theoretical investigation of the SRG–concrete bond is reported in this paper. In particular, the bond performances of SRG-to-concrete joints, which varies the width of the SRG fabric, the displacement rate, and the applied load eccentricity, are analyzed for the stress that is associated with the bond capacity, slip, and failure modes based on the results that are obtained by direct single-lap shear tests. To assess a data set for model calibration, the findings of this paper and those in the technical literature are collected. Therefore, a machine learning (ML) approach that is based on an artificial neural networks (ANN) algorithm is implemented, and a new analytical formulation for the prediction of the SRG-to-concrete bond capacity is proposed.