A study of applying nested hyper-rectangle learning model to concrete strength estimation

Abstract

The aim of this paper is to demonstrate an exemplar-based nested hyper-rectangle learning model (NHLM) and apply it to estimate the strength of high performance concrete (HPC). The proposed model is based on the concept of seeding training data in the Euclidean<i style=""> i</i>-space (where <i style="">i</i> denotes the number of features) as hyper-rectangles. The well-designed structures and one-shot learning procedures of NHLM could adjust learning abilities efficiently when new examples are added. HPC is a highly complex material, which makes modelling its behaviour a very difficult task. Compared with a standard back-propagation neural network (BPN), the experimental results indicate that NHLM provides a powerful tool for estimating the strength of HPC.