A NEURAL NETWORK APPROACH FOR THE ANALYSIS OF LIMIT BEARING CAPACITY OF CONTINUOUS BEAMS DEPENDING ON THE CHARACTER OF THE LOAD

Miloš D Bogdanović, Žarko Petrović, Bojan Milošević, Marina Mijalković, Leonid Stoimenov

DOI Number
-
First page
115
Last page
130

Abstract


Limit analysis is a structural analysis field which is dedicated to the development of efficient methods to directly determine estimates of the collapse load of a given structural model. For this purpose, the field of limit analysis is based on a set of theorems, referred to as limit (bound) theorems, which are a set of theorems based on the law of conservation of energy and are used for a direct definition of the limit state function for failure by plastic collapse or by inadaptation. This study proposes an artificial neural network (ANN) model in order to approximate the residual bending moment, limit and the incremental failure force of continuous beams. The neural network structure applied here is a radial-Gaussian network architecture (RGIN) and complementary training procedure. It is shown on the example of the two-span continuous beam loaded in the middle of the span that the limit and the incremental failure force can be obtained using neural network approach with sufficient precision and is especially suitable in analysis when some of the model parameters are variable.

Full Text:

PDF

References


J. McCarthy, What is Artificial Intelligence?. Computer Science Department of Stanford University, California, United States of America, 2007, available from: http://www-formal.stanford.edu/jmc/whatisai/

C.T. Leondes, Expert Systems, Volume I. Academic Press, San Diego, California 92101-4495, USA, 2002

P. Lu, P.; S. Chen, and Y. Zheng, Artificial intelligence in civil engineering. Mathematical Problems in Engineering, vol. 2012, Article ID 145974, (2012) 22 pages

M.Y. Rafiq, G. Bugmann, and D.J. Easterbrook, Neural Network Design for Engineering Applications. Comput. Struct., 79 (17), (2001) pp. 1541-1552

Z. Waszczyszyn, and L. Ziemianski, Neural Networks in Mechanics of Structures and Materials-New Results and Prospects of Applications. Comput. Struct., 79 (22), (2001) pp.2261-2276

N. Ahmadi, R. Kamyab Moghadas, A. Lavaei, Dynamic analysis of structures using neural networks. American Journal of Applied Sciences 5.9, 1251, (2008)

I. Lou, Y. Zhao, Sludge bulking prediction using principle component regression and artificial neural network. Mathematical Problems in Engineering 2012, Article ID 237693, (2012) 17 pages

E. Bojórquez, J. Bojórquez, S.E. Ruiz, and A. Reyes-Salazar, Prediction of inelastic response spectra using artificial neural networks. Mathematical Problems in Engineering 2012. Article ID 937480, (2012)

J.H. Garrett, Where and why artificial neural networks are applicable in civil engineering. American Society of Civil Engineers, (1994) 129-130

J. Ghaboussi, J.H. Garrett and X. Wu, Knowledge-based modeling of material behavior with neural networks. J. Eng. Mechanics 117, (1991) pp.132–151

S. Arangio, J. Beck, Bayesian neural networks for bridge integrity assessment. Structural Control & Health Monitoring, vol. 19, no. 1(2012) pp. 3–21

P.B. Cachim, Using artificial neural networks for calculation of temperatures in timber under fire loading. Construction and Building Materials, vol. 25, no. 11, (2011) pp. 4175–4180

J. Liu, H. Li, He, C. Concrete Compressive Strength Prediction Using Rebound Method with Artificial Neural Network. Advanced Materials Research Vols. 443-444 (2012) pp. 34-39

M.Y. Cheng, H.C. Tsai, E. Sudjono, Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network. International Journal of Project Management, vol. 29, no. 3(2011) pp. 349–356

X.Z. Wang, X.C. Duan, J.Y. Liu, Application of neural network in the cost estimation of highway engineering. Journal of Computers, vol. 5, no. 11 (2010) pp. 1762–1766

X. Gui, X. Zheng, J. Song, X. Peng, Automation bridge design and structural optimization. Applied Mechanics and Materials, vol. 63-64, (2011) pp. 457–460

D.R. Parhi, A.K. Dash, Application of neural networks and finite elements for condition monitoring of structures. Proceedings of the Institution of Mechanical Engineers C, vol. 225, no. 6 (2011) pp. 1329–1339

S.N. Alacali, B. Akba, B. Doran, Prediction of lateral confinement coefficient in reinforced concrete columns using neural network simulation. Applied Soft Computing Journal, vol. 11, no. 2(2011) pp. 2645–2655

H. Rahman, K. Alireza, G. Reza, Application of artificial neural network, kriging, and inverse distance weighting models for estimation of scour depth around bridge pier with bed sill. Journal of Software Engineering and Applications, vol. 3, no. 10 (2010)

J. Zhang, F. Haghighat, Development of Artificial Neural Network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers. Energy and Buildings, vol. 42, no. 4(2010) pp. 435–440

S. Narasimhan, Robust direct adaptive controller for the nonlinear highway bridge benchmark. Structural Control Health Monitoring, 16(2009)pp. 599–612. doi: 10.1002/stc.337,

T.L. Lee, H.M. Lin, Y.P. Lu, Assessment of highway slope failure using neural networks. Journal of Zhejiang University: Science A, vol. 10, no. 1(2009) pp. 101–108

S. Laflamme, J.J. Connor, Application of self-tuning Gaussian networks for control of civil structures equipped with magnetorheological dampers. Active and Passive Smart Structures and Integrated Systems 2009, vol. 7288 of Proceedings of the SPIE / The International Society for Optical Engineering, March 2009.

A. Bilgil, H. Altun, Investigation of flow resistance in smooth open channels using artificial neural networks. Flow Measurement and Instrumentation, vol. 19, no. 6(2008) pp. 404–408

I. Flood, Towards the next generation of artificial neural networks for civil engineering. Advanced Engineering Informatics, vol. 22, no. 1(2008) pp. 4–14

I. Flood, N. Kartam, Artificial Neural Networks for Civil Engineering: Fundamentals and Applications. ASCE / 1997. 19-43

R. Hecht-Nielsen, Neurocomputing. Addison-Wesley, New York, 1990.

C.R. Alavala, Fuzzy Logic and Neural Networks: Basic Concepts & Applications, New Age International Limited, 2008.

I. Flood, A Gaussian-based feedforward network architecture and complementary training algorithm/ Proceedings of International Joint Conference on Neural Networks, IEEE and INNS / Singapore, 1991, pp. 171-176

N. Gagarin, I. Flood, P. Albrech, Computing truck attributes with artificial neural network. Journal of Computing in Civil Engineering (ASCE), 8(2) (1994)pp.179–200

S. Chen, C.F.N. Cowan, P.M. Grant, Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Transactions on Neural Networks, Vol. 2, Num. 2, (1991)

G.V. Kazinczy, Kiserletek befalazott tartokkal.Betonszemle, 2, 1914.

N.C. Kist, Leidt een Sterkteberekening, die Uitgaat van de Evenredigheid van Kracht en Vormverandering, tot een goede Constructie van Ijzeren Bruggen en gebouwen. Inaugural Dissertation, Polytechnic Institute, Delft, 1917.

P.G. Hodge, Plastic Analysis of Structures, McGraw-Hill, New York, 1959.

J. Baker, J. Heyman, Plastic Design of Frames. Vol 1. Fundamentals, London, Cambridge University Press(1969)

M. Zyczkowski, Combined loadings in the theory of plasticity. Polish Scientific Publishers, PWN and Nijhoff, (1981)

M. Save, Atlas of limit loads of mrtal plates shells and disks. ElsevierScience BV, (1995)

A. J. Konig, Shakedown of Elastic-Plastic Structures, Institute of fundamental Technological Research, Polish Academi of Sciences, Elsevier, 1987.

B.G. Neal, The Plastic Methods of Structural Analysis. London, Chapman and Hall, 1977.

B. Milošević, Analiza granične nosivosti linijskih nosača primenom metode adaptacije. Master Thesis, Faculty of Civil Engineering and Architecture, University of Niš, 2010.

W.T. Koiter, General theorems for elastic–plastic solids. Progress in Solid Mechanics, Amsterdam: North-Holland, 1960. pp.165–221

R. Hill, On the state of stress in a plastic rigid body at the yield point. Philosophical Magazine, Vol. 42 (1951) pp. 868-875

D.C. Drucker, W. Prager, H.J. Greenberg, Extended limit design theorems for continuous media. Quart. Appl. Math., 9 (1952) pp.381-392

M.R. Horne, Fundamental propositions in the plastic theory of structures. J. Inst. Civil Engrs. Vol.34 (1950) pp.174-177

H.J. Greenberg, W. Prager, On limit design of beams and frames. Trans. Am. Soc. Civil Engrs, (First published as Tech. Rep. A18-1, Brown Univ., (1949)) (1952) pp.117-447

B.G. Neal. P.S. Symonds, The Calculation of Collapse Loads for Framed Structures. J. Inst. Civil Engrs, 35(1950-51) pp.21-40,

E. Melan, Zur Plastizitat des raumlichen continuum. Ing. Arch. 9, (1938) pp.116–126

B. Milošević, M. Mijalković, Ž. Petrović, M. Hadžimujović, The Application of the Limit Analysis Theorem and the Adaptation Theorem for Determining the Failure Load of Continuous Beams. Scientific Tehnical Review, vol 60, no 3-4 (2010) pp. 82-92

A.A. Gvozdev, The determination of the value of the collapse load for statically indeterminate systems undergoing plastic deformation. Proceedings of the Conference on Plastic Deformations / Akademiia Nauk S.S.S.R., Moscow-Leningrad, 1398, 19, (tr., R.M.Haythornthwaite, Int. J. Mech.Sci., 1, (1960), 332).

B.G. Neal, P.S. Symonds, The calculation of plastic collapse load for plane frames. prelim. Publ., 4th Congr. Int. Assoc. Bridge Struct. Eng. / Cambridge, 1952., 75

M. Jirásek, Z.P. Bažant, Inelastic Analysis of Structures. John Wiley & Sons, England, 2002.

L.M. Kachanov, Foundations of the Theory of plasticity. North-Holland publishing company - Amsterdam-London, 1971.

Ž. Petrović, Granično stanje loma statički neodređenih rešetkastih nosača. Master Thesis, Faculty of Civil Engineering and Architecture, University of Niš, 2011.

W.F. Chen, D.J. Han, Plasticity for structural engineers. J. Ross publishing classics,2007., ISBN 978-1-932159-75-2


Refbacks

  • There are currently no refbacks.


ISSN: 0353-3670