Hybrid machine learning-based prediction model for the bond strength of corroded Cr alloy-reinforced coral aggregate concrete

Abstract

This study investigated the bond–slip behavior between newly developed Cr alloy steel bars and coral aggregate concrete (CAC) on the basis of experimental and hybrid machine learning. Several variable parameters were examined: (a) corrosion ratio, (b) diameter, (c) CAC strength, (d) anchorage length, and (e) relative protective layer thickness. Results showed that bond strength is positively correlated with relative protective layer thickness and CAC strength, whereas it is negatively correlated with diameter and anchorage length. The bond strength initially rose and subsequently fell as the corrosion ratio increased, with a critical threshold of 1%. The bond–slip curve of Cr–CAC had four stages: microslip, slip, declining, and residual stages. Additionally, the empirical, particle swarm optimization backpropagation (PSO-BP), BP, and support vector machine regression (SVR) models were evaluated using various performance evaluation metrics. The PSO-BP model outperformed the empirical, BP, and SVR models in all evaluation metrics. Compared with the PSO-BP model, the BP and SVR models had a 6.12% and 2.0% reduction in R2, 87.67% and 58.90% increase in root mean square error, 52.27% and 4.54% increase in mean absolute error, 254.72% and 152.83% increase in mean square error, and 76.52% and 52.22% increase in mean absolute percentage error, respectively. The PSO-BP model established in this research can quickly and accurately predict the bond strength of corroded Cr–CAC, providing a rapid assessment method for its application.