Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

Authors:
Fatih Özcan;Cengiz D. Atiş;Okan Karahan;Erdal Uncuoğlu;Harun Tanyildizi
Affiliations:
Civil Engineering Department, Niğde University, 51100 Niğde, Turkey;Civil Engineering Department, Erciyes University, 38039 Kayseri, Turkey;Civil Engineering Department, Erciyes University, 38039 Kayseri, Turkey;Civil Engineering Department, Cukurova University, 01330 Adana, Turkey;Construction Education Department, Fırat University, 23100 Elazığ, Turkey
Venue:
Advances in Engineering Software
Year:
2009

Citing 6
Cited 4

Multilayer feedforward networks are universal approximators

Neural Networks
Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

Neural Networks
Practical neural network recipes in C++

Practical neural network recipes in C++
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Using neural networks to predict workability of concrete incorporating metakaolin and fly ash

Advances in Engineering Software - Civil-comp 2001
Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks

Advances in Engineering Software

Artificial neural network approaches for prediction of backwater through arched bridge constrictions

Advances in Engineering Software
Prediction of spontaneous heating susceptibility of Indian coals using fuzzy logic and artificial neural network models

Expert Systems with Applications: An International Journal
Support vector regression based shear strength modelling of deep beams

Computers and Structures
A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

Advances in Engineering Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, an artificial neural network (ANN) and fuzzy logic (FL) study were developed to predict the compressive strength of silica fume concrete. A data set of a laboratory work, in which a total of 48 concretes were produced, was utilized in the ANNs and FL study. The concrete mixture parameters were four different water-cement ratios, three different cement dosages and three partial silica fume replacement ratios. Compressive strength of moist cured specimens was measured at five different ages. The obtained results with the experimental methods were compared with ANN and FL results. The results showed that ANN and FL can be alternative approaches for the predicting of compressive strength of silica fume concrete.