1. bookVolumen 38 (2016): Edición 2 (June 2016)
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eISSN
2083-831X
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09 Nov 2012
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Application of artificial neural networks to predict the deflections of reinforced concrete beams

Publicado en línea: 15 Jul 2016
Volumen & Edición: Volumen 38 (2016) - Edición 2 (June 2016)
Páginas: 37 - 46
Detalles de la revista
License
Formato
Revista
eISSN
2083-831X
Primera edición
09 Nov 2012
Calendario de la edición
4 veces al año
Idiomas
Inglés
Abstract

Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies.

Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

Keywords

[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

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