Analyzing the compressive strength of clinker mortars using approximate reasoning approaches - ANN vs MLR
Küçük Resim Yok
Tarih
2015
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Techno-Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) models were discussed to determine the compressive strength of clinker mortars cured for 1, 2, 7 and 28 days. In the experimental stage, 1288 mortar samples were produced from 322 different clinker specimens and compressive strength tests were performed on these samples. Chemical properties of the clinker samples were also determined. In the modeling stage, these experimental results were used to construct the models. In the models tricalcium silicate (C3S), dicalcium silicate (C2S), tricalcium aluminate (C(3)A), tetracalcium alumina ferrite (C(4)AF), blaine values, specific gravity and age of samples were used as inputs and the compressive strength of clinker samples was used as output. The approximate reasoning ability of the models compared using some statistical parameters. As a result, ANN has shown satisfying relation with experimental results and suggests an alternative approach to evaluate compressive strength estimation of clinker mortars using related inputs. Furthermore MLR model showed a poor ability to predict.
Açıklama
Emiroglu, Mehmet/0000-0002-0214-4986
WOS: 000352071900006
WOS: 000352071900006
Anahtar Kelimeler
clinker, prediction, compressive strength, artificial neural networks, multi linear regression
Kaynak
Computers And Concrete
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
15
Sayı
1
