Paper Information

Journal:   INTERNATIONAL JOURNAL OF OPTIMIZATION IN CIVIL ENGINEERING   2017 , Volume 7 , Number 1; Page(s) 71 To 80.
 
Paper: 

A COMPREHENSIVE STUDY ON THE CONCRETE COMPRESSIVE STRENGTH ESTIMATION USING ARTIFICIAL NEURAL NETWORK AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

 
 
Author(s):  BEHFARNIA K.*, KHADEMI F.
 
* DEPARTMENT OF CIVIL ENGINEERING, ISFAHAN UNIVERSITY OF TECHNOLOGY, ISFAHAN 84156-83111, IRAN
 
Abstract: 

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.

 
Keyword(s): CONCRETE, COMPRESSIVE STRENGTH, ANFIS, ANN
 
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