Application of artificial neural networks to predict compressive strength of high strength concrete

International Journal of Physical Sciences

Volumn 6, Issue 5, 2011, Pages 975-981

  Hakim, Seyed Jamalaldin Seyed ^a   Noorzaei, Jamaloddin ^b   Jaafar, M S ^b   Jameel, Mohammed ^a   Mohammadhassani, Mohammad ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

^b UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

Artificial neural networks (ANNS); High strength concrete (HSC); Multilayer feedforward neural networks (MFNNS); Relative percentage error (RPE); Root mean square error (RMSE)

Indexed keywords

EID: 79956213944     PISSN: 19921950     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (22)

1. Ashour AF, Alqedra MA (2005). Concrete breakout strength of single anchors in tension using neural networks. J. Adv. Eng. Software, 36: 87-97.
2. Bai J, Wild S, Ware JA, Sabir BB (2003). Using neural networks to predict workability of concrete incorporating metakaolin and fly ash. J. Adv. Eng. Software, 34: 663-669.
3. Dias WPS, Pooliyadda SP (2001). Neural networks for predicting properties of concretes with admixtures. J. Constr. Build. Mater., 15:371-379.
4. Eldin NN, Senouci AB (1994). Measurement and prediction of the strength of rubberized concrete. J. Cem. Concrete Comp., 16: 287-298.
5. Guang NH, Zong WJ (2000). Prediction of compressive strength of concrete by neural networks. J. Cem. Concrete Res., 30: 1245-1250.
6. Hadi M (2003). Neural networks applications in concrete structures. J. Comp. Struct., 81: 373-381.
7. Hakim SJS (2006). Development and applications of artificial neural network for prediction of ultimate bearing capacity in soil and compressive strength of concrete. Master Thesis, Department of Civil Engineering, University Putra Malaysia, Malaysia.
8. Hola J, Schabowicz K (2005). Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests. J. Civil Eng. Manage., 11(1): 23-32.
9. Hola J, Schabowica K (2004). New technique of nondestructive assessment of concrete strength using artificial intelligence. J. NDT&E Int., 38: 251-259.
10. Kasperkiewicz J (2000). The application of ANNs in certain materials analysis problems. J. Mater. Process. Tech., 106: 74-79.
11. Kasperkiewics J, Racz J, Dubrawski A (1995). HPC strength prediction using ANN. ASCE. J. Comp. Civil Eng., 4: 279-284.
12. Kim JI, Kim DK, Feng MQ, Yazdani F (2004). Application of neural networks for concrete strength. J. Mater. Civil Eng., 16(3): 257-264.
13. Kim CY, Bae GJ, Hong SW, Park CH, Moon HK, Shin HS (2001). Neural network based prediction of ground surface settlements due to Tunneling. J. Comp. Geotech., 28: 517-547.
14. Lai S, Sera M (1997).Concrete strength prediction by means of neural network. J. Constr. Build. Mater., 11(2): 93-98.
15. Lee SC (2003). Prediction of concrete strength using artificial neural networks. J. Eng. Struct., 25: 849-857.
16. Mansour MY, Dicleli M, Lee YJ, Zhang J (2004). Predicting the shear strength of reinforced concrete beams using artificial neural networks. J. Eng. Struct,. 26: 781-799.
17. Noorzaei J, Hakim SJS, Jaafar MS, Abang AAA, Thanon WAM (2007). An optimal architecture of artificial neural network for predicting compressive strength of concrete. J. Indian Concrete, 81(8): 17-24.
18. Okine NOA, Fekpel ESK (1996). Strength characteristics modeling of lateritic soils using adaptive neural networks. J. Constr. Build. Mater., 10(8): 577-582.
19. Oztas A, Pala M, Ozbay E, Kanca E, Caglar N, Bhatti MA (2005). Predicting the compressive strength and slump of high strength concrete using neural network. J. Constr. Build. Mater., 20(9): 769-775.
20. Pala M, Ozbay E, Oztas A, Yuce MI (2005). Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. J. Constr. Build. Mater., 21: 384-394.
21. Yeh IC (1998). Modeling of strength of HPC using ANN. Cem. Concrete Res. J., 28(12): 1797-1808.
22. Yeh YC, Kuo YH, Hsu, DS (1992). Building an expert system for debugging FEM input data with artificial neural networks. J. Expert Syst. Appl., 5: 59-70.