Bond prediction of stainless-steel reinforcement using artificial neural networks

Abstract

Stainless-steel reinforcement has become increasingly popular in the construction industry in recent years, mainly due to its distinctive characteristics and excellent mechanical properties. There is a real need to develop a fundamental understanding of the bond behaviour of stainless-steel-reinforced concrete (RC). This paper investigates the bond behaviour of stainless-steel-RC using the advancement of artificial neural networks (ANNs) and compares the performance with experimental data available in the literature with reference to existing bond design rules according to international design standards. Accordingly, a new bond design formula is proposed to predict the bond strength capacity of stainless-steel reinforcement. The results show an excellent agreement between the experimental results and the predictions of the ANN model. Both Eurocode 2 and model code 2010 are shown to be extremely conservative compared with ANN predictions. The proposed ANN-based formula provides an excellent basis for engineers to specify bond strength of stainless-steel reinforcement in RC members in an efficient and sustainable manner, with minimal wastage of materials.