Mechanical properties prediction in rebar using kernel-based regression models

Abstract

A successful application of neural networks to the prediction of four important mechanical properties of steel rebar used in civil construction has been reported recently. In the current work, we advanced further in this issue by evaluating the performances of three kernel-based regression models, namely, the minimal learning machine (MLM), the support vector regression (SVR), and the least-squares SVR (LSSVR) in the estimation of the yield strength (YS), ultimate tensile strength (UTS), UTS/YS ratio, and percent elongation (PE) from chemical composition and parameters used during hot rolling and heat treatment. The achieved results indicate that the LSSVR model consistently outperforms the SVR and MLM models for all four properties studied.