A robust neural network classifier to model the compressive strength of high performance concrete using feature subset selection (original) (raw)

2012

Abstract

ABSTRACT High performance concrete (HPC) is a mixture of cement, fine aggregate, coarse aggregate, water and other ingredients. Modeling the compressive strength of HPC (or concrete strength) is a difficult task in building materials because it is influenced by the proportions of various ingredients within the HPC. Researchers have tried to confront this difficulty by modeling the strength of concrete using artificial neural networks (ANN). The influence of ingredients on concrete strength still remains unknown due to the "black box" nature of ANN. This paper investigates the influence of ingredients on concrete strength modeling using feature selection. A robust ANN-model using the knowledge from feature selection has been developed to predict the strength effectively. Experimental results have shown that the proposed model is efficient with respect to runtime and prediction.

Dr. Venu Malagavelli hasn't uploaded this paper.

Let Dr. Venu know you want this paper to be uploaded.

Ask for this paper to be uploaded.