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

Neural Computing and Applications

Volumn 19, Issue 4, 2010, Pages 507-513

  Basyigit C ^a   Akkurt, Iskender ^a   Kilincarslan, S ^a   Beycioglu, A ^b  

^a SULEYMAN DEMIREL UNIVERSITY   (Turkey)

^b DUZCE UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Baryte; Compressive strength; Computer simulation; Fuzzy logic; Heavyweight concrete

Indexed keywords

EID: 77952884779     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-009-0292-9     Document Type: Article

References (23)

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: 83-86.
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: 815.
5. Topçu IB, Sengel S (2004) Properties of concretes produced with waste concrete aggregate. Cement Concrete Res 34-38: 1307.
6. Topçu IB, Canbaz M (2004) Properties of concretes containing waste glass. Cement Concrete Res 34-42: 267.
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: 334-340.
8. Demir F (2008) Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Construction Build Mater 22: 1428-1435.
9. Topçu IB, Sari{dotless}demir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 41: 305-311.
10. Sari{dotless}demir 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: 1279-1286.
11. Lee S-C (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25: 849-857.
12. Parichatprecha R, Nimityongskul P (2009) Analysis of durability of high performance concrete using artificial neural networks. Construction Build Mater 23: 910-917.
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): 856-863.
14. Topçu IB, Sari{dotless}demir M (2008) Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Construction Build Mater 22: 532-540.
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: 751-763.
17. Topçu IB, Sari{dotless}demir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic. Comput Mater Sci 41(3): 305-311.
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: 427-436.
20. Zadeh LA (1965) Fuzzy Sets. Inf Control 8: 338-352.
21. Topçu IB, Sari{dotless}demir M (2008) Prediction of Rubberized concrete properties using artificial neural networks and fuzzy logic. Construction Build Mater 22(4): 532-540.
22. Beycioǧlu A (2008) Modelli{dotless}ng the effects of industri{dotless}al wastes on properti{dotless}es of li{dotless}ghtwei{dotless}ght concrete by fuzzy logi{dotless}c 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: 590-593.