Neural networks for inelastic mid-span deflections in continuous composite beams

- Journal title : Structural Engineering and Mechanics
- Volume 36, Issue 2, 2010, pp.165-179
- Publisher : Techno-Press
- DOI : 10.12989/sem.2010.36.2.165

Title & Authors

Neural networks for inelastic mid-span deflections in continuous composite beams

Pendharkar, Umesh; Chaudhary, Sandeep; Nagpal, A.K.;

Pendharkar, Umesh; Chaudhary, Sandeep; Nagpal, A.K.;

Abstract

Maximum deflection in a beam is a design criteria and occurs generally at or close to the mid-span. Neural networks have been developed for the continuous composite beams to predict the inelastic mid-span deflections (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage, in concrete) from the elastic moments and elastic mid-span deflections (neglecting instantaneous cracking and time effects). The training and testing data for the neural networks is generated using a hybrid analytical-numerical procedure of analysis. The neural networks have been validated for four example beams and the errors are shown to be small. This methodology, of using networks enables a rapid estimation of inelastic mid-span deflections and requires a computational effort almost equal to that required for the simple elastic analysis. The neural networks can be extended for the composite building frames that would result in huge saving in computational time.

Keywords

neural network;creep;shrinkage;composite beam;concrete cracking;

Language

English

Cited by

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Prediction of moments in composite frames considering cracking and time effects using neural network models,;;;

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Rapid prediction of long-term deflections in composite frames,;;;;

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References

1.

Adeli, H. (2001), "Neural networks in civil engineering, 1989-2000", Comput-Aided Civ. Inf., 16(2), 126-147.

2.

Akbas, B. (2006), "A neural network model to assess the hysteretic energy demand in steel moment resisting frames", Struct. Eng. Mech., 23(2), 177-193.

3.

Arslan, H., Ceylan, M., Kaltakci, M.Y., Ozaby, Y. and Gulay, F.G. (2007), "Prediction of force reduction factor (R) of prefabricated industrial buildings using neural networks", Struct. Eng. Mech., 27(2), 117-134.

4.

Bakhary, N., Hao, H. and Deeks, A.J. (2007), "Damage detection using artificial neural network with consideration of uncertainties", Eng. Struct., 29(11), 2806-2815.

5.

Bazant, Z.P. (1972), "Prediction of concrete creep-effects using age adjusted effective modulus method", ACI J., 69(4), 212-217.

6.

CEB-FIP MC 90 (1993), "Model code 1990 for concrete structures. Bulletin d information No. 213/214", Comite Euro International du Beton-Fe'de'ration International de la Pre'contrainte, Laussane (Switzerland).

7.

Chandak, R., Upadhyay, A. and Bhargava, P. (2008), "Shear lag prediction in symmetrical laminated composite box beams using artificial network", Struct. Eng. Mech., 29(1), 77-89.

8.

Chaudhary, S., Pendharkar, U. and Nagpal, A.K. (2007a), "Bending moment prediction for continuous composite beams by neural networks", Adv. Struct. Eng., 10(4), 439-454.

9.

Chaudhary, S., Pendharkar, U. and Nagpal, A.K. (2007b), "A hybrid procedure for cracking and time-dependent effects in composite frames at service load", J. Struct. Eng.-ASCE, 133(2), 166-175.

10.

Chaudhary, S., Pendharkar, U. and Nagpal, A.K. (2007c), "An analytical-numerical procedure for cracking and time-dependent effects in continuous composite beams under service load", Steel Compos. Struct., 7(3), 219-240.

11.

Cheng, J., Cai, C.S. and Xiao, R.C. (2007), "Application of artificial neural networks to the response prediction of geometrically nonlinear truss structures", Struct. Eng. Mech., 26(3), 251-262.

12.

Cho, H.N., Cho, Y.M., Lee, S.C. and Hur, C.K. (2004), "Damage assessment of cable stayed bridges using probabilistic neural network", Struct. Eng. Mech., 17(3), 483-492.

13.

Flood, I. and Kartam, N. (1994a), "Neural networks in civil engineering I: Principles and understanding", J. Comput. Civil Eng.-ASCE, 8(2), 131-148.

14.

Flood, I. and Kartam, N. (1994b), "Neural networks in civil engineering II: Systems and application", J. Comput. Civil Eng.-ASCE, 8(2), 149-162.

15.

Gilbert, R.I. and Bradford, M.A. (1995), "Time-dependent behaviour of composite beams at service loads", J. Struct. Eng.-ASCE, 121(2), 319-327.

16.

Hajela, P. and Berke, L. (1991), "Neurobiological computational models in structural analysis and design", Comput. Struct., 41(4), 657-667.

17.

Hajela, P. and Berke, L. (1992), "Neural networks in engineering analysis and design", Comput. Syst. Eng., 3(1), 525-538.

18.

Jiang, S.F., Zhang, C.M. and Koh, C.G. (2006), "Structural damage detection by integrating data fusion and probabilistic neural network", Adv. Struct. Eng., 9(4), 445-458.

19.

Jeng, C.H. and Mo, Y.L. (2004), "Quick seismic response estimation of prestressed concrete bridges using artificial neural networks", J. Comput. Civil Eng.-ASCE, 118(4), 360-369.

20.

Lee, J.J., Lee, J.W., Yi, J.H., Yun, C.B. and Jung, H.Y. (2005), "Neural network-based damage detection for bridges considering errors in baseline finite element models", J. Sound Vib., 280(3-5), 555-578.

21.

Maru, S. and Nagpal, A.K. (2004), "Neural network for creep and shrinkage deflections in reinforced concrete frames", J. Comput. Civil Eng.-ASCE, 18(4), 350-359.

22.

Mo, Y.L. and Lin, S.S. (1994), "Investigation of framed shearwall behavior with neural networks", Mag. Concrete Res., 46(169), 289-300.

23.

Mo, Y.L. and Han, R.H. (1995), "Investigation of prestressed concrete frame behavior with neural networks", J. Intel. Mat. Syst. Str., 6, 566-573.

24.

Mo, Y.L. and Koan, K.J. (1998), "Investigation of welding effect on rebars using neural networks", J. Test. Eval., 26(3), 285-292.

25.

Mo, Y.L., Hung, H.Y. and Zhong, J. (2002), "Investigation of stress-strain relationship of confined concrete in hollow bridge columns using neural networks", J. Test. Eval., 30(4), 330-339.

26.

Pendharkar, U. (2007), "Neural network model for composite beams and frames considering cracking and timeeffects", Ph.D. Thesis, IIT Delhi, Delhi.

27.

Qu, W.L., Chen, W. and Xiao, Y.Q. (2003), "A two-step approach for joint damage diagnosis of framed structures using artificial neural networks", Struct. Eng. Mech., 16(5), 581-596.

28.

Reich, Y. and Barai, S.V. (1999), "Evaluating machine learning models for engineering problems", Artif. Intell. Eng., 13(3), 257-272.

29.

SNNS (1998), User's Manual, Ver. 4.2, University of Sttutgart, Institute for Parallel and Distributed High Performance Systems.

30.

Subrmanian, K., Mini, K. and Florence, J.K. (2005), "Neural network based modeling of infilled steel frames", Struct. Eng. Mech., 21(5), 495-506.

31.

Tsai, C.H. and Hsu, D.S. (2002), "Diagnosis of reinforced concrete structural damage base on displacement time history using the back-propagation neural network technique", J. Comput. Civil Eng.-ASCE, 6(1), 49-58.