nach oben

Neural Computing and Applications

Erschienen in:

01.06.2010 | Original Article

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

verfasst von: C. Başyigit, Iskender Akkurt, S. Kilincarslan, A. Beycioglu

Erschienen in: Neural Computing and Applications | Ausgabe 4/2010

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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).

Nächster Artikel Generalized fuzzy interior ideals of semigroups

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Turkish code 11222 (1980) Concrete, ready concrete. TSE, Ankara (in Turkish)

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

Varshney RS (1982) Concrete technology, 2nd edn. Oxford & IBH Publishing Co., New Delhi

Topçu IB (2003) Properties of heavyweight concrete produced with baryte. Cement Concrete Res 33–36:815CrossRef

Topçu IB, Sengel S (2004) Properties of concretes produced with waste concrete aggregate. Cement Concrete Res 34–38:1307CrossRef

Topçu IB, Canbaz M (2004) Properties of concretes containing waste glass. Cement Concrete Res 34–42:267CrossRef

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

Demir F (2008) Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Construction Build Mater 22:1428–1435CrossRef

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.

Zadeh LA (1965) Fuzzy Sets. Inf Control 8:338–352MATHCrossRefMathSciNet

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

Titel: Prediction of compressive strength of heavyweight concrete by ANN and FL models
verfasst von: C. Başyigit
Iskender Akkurt
S. Kilincarslan
A. Beycioglu
Publikationsdatum: 01.06.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 4/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0292-9

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 4/2010

Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor

Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake

Limit cycle prediction of a neurocontrol vehicle system based on gain-phase margin analysis

Chaotic neural network controlled by particle swarm with decaying chaotic inertia weight for pattern recognition

A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line

Algebraic condition of synchronization for multiple time-delayed chaotic Hopfield neural networks

Premium Partner