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Neural Computing and Applications
Published in: Neural Computing and Applications 4/2010

01-06-2010 | Original Article

Prediction of compressive strength of heavyweight concrete by ANN and FL models

Authors: C. Başyigit, Iskender Akkurt, S. Kilincarslan, A. Beycioglu

Published in: Neural Computing and Applications | Issue 4/2010

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Abstract

The compressive strength of heavyweight concrete which is produced using baryte aggregates has been predicted by artificial neural network (ANN) and fuzzy logic (FL) models. For these models 45 experimental results were used and trained. Cement rate, water rate, periods (7–28–90 days) and baryte (BaSO4) rate (%) were used as inputs and compressive strength (MPa) was used as output while developing both ANN and FL models. In the models, training and testing results have shown that ANN and FL systems have strong potential for predicting compressive strength of concretes containing baryte (BaSO4).
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Literature
1.
Turkish code 11222 (1980) Concrete, ready concrete. TSE, Ankara (in Turkish)
2.
Kilincarslan S, Akkurt I, Basyigit C (2006) The effect of baryte rate on some physical and mechanical properties of concrete. Mater Sci Eng A 424:83–86CrossRef
3.
Varshney RS (1982) Concrete technology, 2nd edn. Oxford & IBH Publishing Co., New Delhi
4.
Topçu IB (2003) Properties of heavyweight concrete produced with baryte. Cement Concrete Res 33–36:815CrossRef
5.
Topçu IB, Sengel S (2004) Properties of concretes produced with waste concrete aggregate. Cement Concrete Res 34–38:1307CrossRef
6.
Topçu IB, Canbaz M (2004) Properties of concretes containing waste glass. Cement Concrete Res 34–42:267CrossRef
7.
Bilim C et al (2009) Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Adv Eng Softw 40:334–340MATHCrossRef
8.
Demir F (2008) Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Construction Build Mater 22:1428–1435CrossRef
9.
Topçu IB, Sarıdemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 41:305–311CrossRef
10.
Sarıdemir M et al (2009) Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic. Construction Build Mater 23:1279–1286CrossRef
11.
Lee S-C (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25:849–857CrossRef
12.
Parichatprecha R, Nimityongskul P (2009) Analysis of durability of high performance concrete using artificial neural networks. Construction Build Mater 23:910–917CrossRef
13.
Özcan F et al (2009) Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Adv Eng Softw 40(9):856–863MATHCrossRef
14.
Topçu IB, Sarıdemir M (2008) Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Construction Build Mater 22:532–540CrossRef
15.
Tanyildizi H (2009) Fuzzy logic model for prediction of mechanical properties of lightweight concrete exposed to high temperature. Mater Des 30:2205–2210
16.
Cevik A, Guzelbey IH (2008) Neural network modeling of strength enhancement for CFRP confined concrete cylinders. Build Environ 43:751–763CrossRef
17.
Topçu IB, Sarıdemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic. Comput Mater Sci 41(3):305–311CrossRef
18.
TS EN 802 (1985) Design of concrete mixture. TSE, Ankara (in Turkish)
19.
Hou T-H, Su C-H, Chang H-Z (2008) Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process. Expert Syst Appl 34:427–436CrossRef
20.
21.
Topçu IB, Sarıdemir M (2008) Prediction of Rubberized concrete properties using artificial neural networks and fuzzy logic. Construction Build Mater 22(4):532–540
22.
Beycioğlu A (2008) Modellıng the effects of industrıal wastes on propertıes of lıghtweıght concrete by fuzzy logıc method. M.Sc. thesis, Construction Education Department, Graduate School of Applied and Natural Science, Süleyman Demirel University
23.
Terzi S (2007) Modeling the pavement service ability ratio of flexible highway pavements by artificial neural networks. Construction Build Mater 21:590–593CrossRef
Metadata
Title
Prediction of compressive strength of heavyweight concrete by ANN and FL models
Authors
C. Başyigit
Iskender Akkurt
S. Kilincarslan
A. Beycioglu
Publication date
01-06-2010
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 4/2010
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-009-0292-9

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