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2015, vol. 58, br. 1, str. 3-16
Procena čvrstoće betona pri pritisku, korišćenjem veštačkih neuronskih mreža
aUniverzitet u Beogradu, Rudarsko-geološki fakultet, Geološki odsek
bUniverzitet u Beogradu, Arhitektonski fakultet

e-adresasrdjan.kostic@rgf.bg.ac.rs, d.vasovic@open.telekom.rs
Projekat:
Magmatizam i geodinamika Balkanskog poluostrva od mezozoika do danas: značaj za obrazovanje metaličnih i nemetaličnih rudnih ležišta (MPNTR - 176016)

Ključne reči: čvrstoća betona; vodocementni faktor; superplastifikator; zamrzavanje/otkravljivanje; starost; veštačka neuronska mreža
Sažetak
U radu se daje procena čvrstoće betona pri pritisku, primenom veštačkih neuronskih mreža s prostiranjem signala unapred i propagacijom greške unazad. Obučavanje mreže sprovodi se korišćenjem Levenberg-Markart algoritma obučavanja za četiri različite arhitekture neuronskih mreža, s jednom jedinicom, tri jedinice, te osam i dvanaest jedinica u skrivenom sloju, radi odbacivanja efekta ,,pretreniranja'. Treniranje, validacija i testiranje neuronskih mreža izvodi se na osnovu rezultata eksperimentalnog ispitivanja čvrstoće pri pritisku na 75 uzoraka betona, s različitim vodocementnim faktorom i količinom superplastifikatora tipa melamina. Ispitivani uzorci betona izlagani su različitim ciklusima zamrzavanja/ otkravljivanja, a njihova čvrstoća pri pritisku određivana je nakon 7, 20 i 32 dana. Dobijeni rezultati ukazuju na to da neuronska mreža s dvanaest jedinica u skrivenom sloju daje ocenu čvrstoće zadovoljavajuće tačnosti u poređenju sa eksperimentalno dobijenim podacima (R≈0,97, MSE=0,005). Rezultati izvedene analize dodatno su potvrđeni sračunavanjem vrednosti standardnih statističkih grešaka: najmanjom vrednošću srednje apsolutne greške (MAPE), varijanse relativne vrednosti apsolutne greške (VARE) i medijane (MEDAE), kao i najvećom vrednošću sračunate varijanse (VAF) za izabranu arhitekturu neuronske mreže.
Reference
*** (1997) SRPS ISO 4109:1997. Svež beton - Određivanje konsistencije - Ispitivanje sleganja
*** (2000) SRPS ISO 4012:2000. Beton - određivanje čvrstoće epruveta pri pritisku
*** (1997) SRPS ISO 4110:1997. Svež beton - Određivanje konsistencije - Ispitivanje po Vebeu
*** (1996) SRPS U.M8.052:1996: Svež beton - Određivanje konsistencije - Ispitivanje tečenja
*** (1993) SRPS U.M1.016:1993: Beton - Ispitivanje otpornosti betona prema dejstvu mraza
*** (2009) SRPS CEN/TR 15177:2009: Ispitivanje otpornosti betona prema zamrzavanju/otkravljivanju - oštećenje unutrašnje strukture
*** (2010) SRPS EN 12390-3:2010: Ispitivanje očvrslog betona - Čvrstoća pri pritisku uzoraka za ispitivanje. Deo 3
Aggarwal, P. (2011) Prediction of compressive strength of self-compacting concrete with fuzzy logicScience. World Academy of Science, Engineering and Technology, 77: 847-854
Al-Mutairi, N., Terro, M., Al-Khaleefi, A. (2004) Effect of recycling hospital ash on the compressive properties of concrete: statistical assessment and predicting model. Building and Environment, 39(5): 557-566
Bai, J., Wild, S., Ware, J.A., Sabir, B.B. (2003) Using neural networks to predict workability of concrete incorporating metakaolin and fly ash. Advances in Engineering Software, 34(11-12): 663-669
Basma, A.A., Barakat, S., Oraimi, S.A. (1999) Prediction of cement degree of hydration using artificial neural networks. Mater J, 96 (2); 166-72
Başyigit, C., Akkurt, I., Kilincarslan, S., Beycioglu, A. (2009) Prediction of compressive strength of heavyweight concrete by ANN and FL models. Neural Computing and Applications, 19(4): 507-513
Berhardt, C.J. (1956) Hardening of concrete at different temperatures. Danish Institute for Building Research, Session B-II
Bilim, C., Atiş, C.D., Tanyildizi, H., Karahan, O. (2009) Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Advances in Engineering Software, 40(5): 334-340
Dias, W.P.S., Pooliyadda, S.P. (2001) Neural networks for predicting properties of concretes with admixtures. Construction and Building Materials, 15(7): 371-379
Jepsen, M.T. (2002) Predicting concrete durability by using artificial neural network. Special NCR publication, ID 5268
Kim, J., Kim, D.K., Feng, M.Q., Yazdani, F. (2004) Application of Neural Networks for Estimation of Concrete Strength. Journal of Materials in Civil Engineering, 16(3): 257-264
Lee, S. (2003) Prediction of concrete strength using artificial neural networks. Engineering Structures, 25(7): 849-857
Lippmman, R. (1987) An introduction to computing with neural nets. IEEE ASSP Magazine, 22. april, 4-22
Looney, C.G. (1996) Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Transactions on Knowledge and Data Engineering, 8(2): 211-226
Monjezi, M., Hasanipanah, M., Khandelwal, M. (2012) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Computing and Applications, 22(7-8): 1637-1643
Mukherjee, A., Biswas, S.N. (1997) Artificial neural networks in prediction of mechanical behavior of concrete at high temperature. Nuclear Engineering and Design, 178(1): 1-11
Nehdi, M., Chabib, H.E., Naggar, M.H.E. (2001) Predicting performance of selfcompacting concrete mixtures using artificial neural. Mater J, 98(5); 349-401
Nelson, M., Illingworth, W.T. (1990) A practical guide to neural nets. Reading MA: Addisin-Wesley
Oh, J-W., Lee, I-W., Kim, J.T., Lee, G-W. (1999) Application of neural networks for proportioning of concrete. Mater J, 96(1); 352-356
Özcan, F., Atiş, C.D., Karahan, O., Uncuoğlu, E., Tanyildizi, H. (2009) Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Advances in Engineering Software, 40(9): 856-863
Pala, M., Özbay, E., Öztaş, A., Yuce, M. (2007) Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Construction and Building Materials, 21(2): 384-394
Plowman, J.M. (1956) Maturity and the strength of concrete. Magazine of Concrete Research, 8(22): 13-22
Popovics, S., Ujhelyi, J. (2008) Contribution to the Concrete Strength versus Water-Cement Ratio Relationship. Journal of Materials in Civil Engineering, 20(7): 459-463
Ramezanianpour, A.A., Sobhani, J., Sobhani, M. (2004) Application of an Adaptive Neuro-Fuzzy System in the Prediction of HPC Compressive Strength. Amirkabir J Sci Technol, 5(59-C): 78-93
Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986) Learning internal representation by error propagation. Rumelhart D.E., Mccleland J.L. [ur.] 1, pp. 318-362
Sarıdemir, M. (2009) Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Advances in Engineering Software, 40(9): 920-927
Sobhani, J., Najimi, M., Pourkhorshidi, A.R., Parhizkar, T. (2010) Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Construction and Building Materials, 24(5): 709-718
Sonmez, H., Gokceoglu, C., Nefeslioglu, H.A., Kayabasi, A. (2006) Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics and Mining Sciences, 43(2): 224-235
Tiryaki, B. (2008) Application of artificial neural networks for predicting the cuttability of rocks by drag tools. Tunnelling and Underground Space Technology, 23(3): 273-280
Topçu, İ.B., Sarıdemir, M. (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science, 41(3): 305-311
Wedding, P.A., Carino, N.J. (1984) The Maturity Method: Theory and Application. Cement, Concrete and Aggregates, 6(2): 61
Yaprak, H., Karacı, A., Demir, İ. (2011) Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks. Neural Computing and Applications, 22(1): 133-141
Yeh, I.C. (1998) Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research, 28(12): 1797-1808
Yi, S., Moon, Y., Kim, J. (2005) Long-term strength prediction of concrete with curing temperature. Cement and Concrete Research, 35(10): 1961-1969
 

O članku

jezik rada: srpski, engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/grmk1501003K
objavljen u SCIndeksu: 20.03.2015.