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.

摘要

最近报道了神经网络在预测土木建筑用钢筋的四种重要机械性能方面的成功应用。在目前的工作中，我们通过评估三种基于内核的回归模型的性能，即最小学习机 (MLM)、支持向量回归 (SVR) 和最小二乘 SVR (LSSVR)，进一步解决了这个问题在根据热轧和热处理过程中使用的化学成分和参数估算屈服强度 (YS)、极限抗拉强度 (UTS)、UTS/YS 比率和伸长率 (PE) 时。取得的结果表明，LSSVR 模型在所研究的所有四个属性方面始终优于 SVR 和 MLM 模型。