Volume 7, Number 1 (1-2017)                   2017, 7(1): 71-80 | Back to browse issues page


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Behfarnia K, Khademi F. A COMPREHENSIVE STUDY ON THE CONCRETE COMPRESSIVE STRENGTH ESTIMATION USING ARTIFICIAL NEURAL NETWORK AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. International Journal of Optimization in Civil Engineering. 2017; 7 (1) :71-80
URL: http://ijoce.iust.ac.ir/article-1-284-en.html

Abstract:   (1322 Views)

This research deals with the development and comparison of two data-driven models, i.e., Artificial Neural Network (ANN) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) models for estimation of 28-day compressive strength of concrete for 160 different mix designs. These various mix designs are constructed based on seven different parameters, i.e., 3/4 mm sand, 3/8 mm sand, cement content, maximum size of aggregate, gravel content, water-cement ratio, and fineness modulus. In this study, it is found that the ANN model is an efficient model for prediction of compressive strength of concrete. In addition, ANFIS model is a suitable model for the same estimation purposes, however, the ANN model is recognized to be more fitting than ANFIS model in predicting the 28-day compressive strength of concrete.

Full-Text [PDF 677 kb]   (1089 Downloads)    
Type of Study: Research | Subject: Optimal design
Received: 2016/07/19 | Accepted: 2016/07/19 | Published: 2016/07/19

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