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Application of artificial neural networks to predict the deflections of reinforced concrete beams


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[1] Kuczyński W., Concrete Structures: Continuum theory of reinforced concrete flexural, [in Polish: Konstrukcje betonowe: kontynualna teoria zginania żelbetu], PWN, Warszawa, 1971.Search in Google Scholar

[2] Ryżyński A., Wołowicki W., The proposal for calculating deflection of reinforced concrete beam with regard to its deformed smoothness, [in Polish: Propozycja obliczania ugięć belki żelbetowej z uwzględnieniem niegładkości jej odkształconej], Archiwum Inżynierii Lądowej, 1968, 2, 329–347.Search in Google Scholar

[3] Borcz A., Theory of reinforced concrete structures, [in Polish: Teoria konstrukcji żelbetowych], Vol. II, Wydawnictwo Politechniki Wrocławskiej, Wrocław, 1986.Search in Google Scholar

[4] Polski Komitet Normalizacyjny. Concrete, reinforced concrete and prestressed structures. Calculations and design [in Polish: Konstrukcje betonowe, żelbetowe i sprężone. Obliczenia statyczne i projektowanie], PN-B-03264:2002, Warszawa, 2002.Search in Google Scholar

[5] Polski Komitet Normalizacyjny. Eurocode 2: Design of concrete structures – Part 1-1: General rules and rules for buildings, [in Polish: Eurokod 2: Projektowanie konstrukcji z betonu – Część 1-1: Reguły ogólne i reguły dla budynków], PN-EN-1992-1-1:2008, Warszawa 2002.Search in Google Scholar

[6] Kubicki J., Deflections of reinforced concrete beams calculated according to PN-84/B-03264 and Eurocode 2.1 methods in comparison with test results, [in Polish: Ugięcie belek żelbetowych obliczone według PN-84/B-03264 i Eurokodu 2.1 w konfrontacji z wynikami badań doświadczalnych], Prace Instytutu Techniki Budowlanej, 1999, 28, 3–26.Search in Google Scholar

[7] McCulloch W., Pitts W., A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, 1943, 5, 115–133.10.1007/BF02478259Search in Google Scholar

[8] Schabowicz K., Neural networks in the NDT identification of the strength of concrete, Archives of Civil Engineering, 2005, 51(3), 371–382.Search in Google Scholar

[9] Schabowicz K., Hoła B., Application of artificial neural networks in predicting earthmoving machinery effectiveness ratios, Archives of Civil and Mechanical Engineering, 2008, 8(4), 73–84.10.1016/S1644-9665(12)60123-XSearch in Google Scholar

[10] Ochmański M., Bzówka J., Back analysis of SCL tunnels based on Artificial Neural Network, Architecture, Civil Engineering, Environment – ACEE Journal, 2012, 3, 73–81.Search in Google Scholar

[11] Guzelbey I.H., Cevik A., Gogus M.T., Prediction of rotation capacity of wide flange beams using neural networks, Journal of Constructional Steel Research, 2006, Vol. 62, 950–961.10.1016/j.jcsr.2006.01.003Search in Google Scholar

[12] Pala M., Caglar N., A parametric study for distortional buckling stress on cold-formed steel using a neural network, Journal of Constructional Steel Research, 2007, Vol. 63, 686–691.10.1016/j.jcsr.2006.07.005Search in Google Scholar

[13] Chaudhary S., Pendharkar U., Nagpal A.K., Bending moment prediction for continuous composite beams by neural networks, Advances in Structural Engineering, 2007, Vol. 10, 439–454.10.1260/136943307783239390Search in Google Scholar

[14] Chaudhary S., Pendharkar U., Nagpal A.K., Neural network for bending moment in continuous composite beams considering cracking and time effects in concrete structures, Engineering Structures, 2007, Vol. 29, 269–279.10.1016/j.engstruct.2006.11.009Search in Google Scholar

[15] Tadesse Z., Patel K.A., Chaudhary S., Nagpal A.K., Neural networks for prediction of deflection in composite bridges, Journal of Constructional Steel Research, 2012, Vol. 68(1), 138–149.10.1016/j.jcsr.2011.08.003Search in Google Scholar

[16] Mohammadhassani M., Nezamabadi-Pour H., Jumaat M.Z., Jameel M., Arumugam A.M.S., Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams, Computers and Concrete, 2013, Vol. 11(3), 237–252.10.12989/cac.2013.11.3.237Search in Google Scholar

[17] Tadeusiewicz R., Neural networks, [in Polish: Sieci neuronowe], Akademicka Oficyna Wydawnicza RM, Warszawa, 1993.Search in Google Scholar

[18] Polski Komitet Normalizacyjny. Metals – Tensile testing – Method of test at ambient temperature, [in Polish: Metale – Próba rozciągania – Metoda badań w temperaturze otoczenia]. PN-EN 10002-1:2004, Warszawa, 2004.Search in Google Scholar

[19] Osowski S., Neural networks in terms of algorithmic, [in Polish: Sieci neuronowe w ujęciu algorytmicznym], Wydawnictwo Naukowo-Techniczne, Warszawa, 1996.Search in Google Scholar

eISSN:
2083-831X
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Geosciences, other, Materials Sciences, Composites, Porous Materials, Physics, Mechanics and Fluid Dynamics