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http://dx.doi.org/10.12989/scs.2015.18.3.547

Rapid prediction of long-term deflections in composite frames  

Pendharkar, Umesh (School of Engineering and Technology, Vikram University)
Patel, K.A. (Civil Engineering Department, Indian Institute of Technology Delhi)
Chaudhary, Sandeep (Civil Engineering Department, Malaviya National Institute of Technology Jaipur)
Nagpal, A.K. (Civil Engineering Department, Indian Institute of Technology Delhi)
Publication Information
Steel and Composite Structures / v.18, no.3, 2015 , pp. 547-563 More about this Journal
Abstract
Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (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 cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.
Keywords
composite frames; cracking; creep; deflection; neural networks;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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1 Bazant, Z.P. (1972), "Prediction of concrete creep-effects using age adjusted effective modulus method", ACI J., 69(4), 212-217.
2 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.   DOI
3 Chaudhary, S., Pendharkar, U. and Nagpal, A.K. (2007b), "Hybrid procedure for cracking and timedependent effects in composite frames at service load", J. Struct. Eng., 133(2), 166-175.   DOI
4 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 Comp. Struct., Int. J., 7(3), 219-240.   DOI   ScienceOn
5 Chaudhary, S., Pendharkar, U., Patel, K.A. and Nagpal, A.K. (2014), "Neural networks for deflections in continuous composite beams considering concrete cracking", Iran. J. Sci. Technol., Trans. Civil Eng., 38(C1+), 205-221.
6 Comite Euro International du Beton-Federation International de la Precontrainte (CEB-FIP) (1993), Model code for concrete structures, Thomas Telford, London, UK.
7 Feng, Q.M., Kim, D.K., Yi, J.H. and Chen, Y. (2004), "Baseline models for bridge performance monitoring", J. Eng. Mech., 130(5), 562-569.   DOI
8 Ghali, A., Favre, R. and Elbadry, M. (2002), Concrete Structures: Stresses and Deformations, (3rd Ed.), Spon Press, London, UK.
9 Gholizadeh, S. and Salajegheh, E. (2010), "Optimal seismic design of steel structures by an efficient soft computing based algorithm", J. Constr. Steel Res., 66(1), 85-95.   DOI   ScienceOn
10 Gupta, V.K., Kwatra, N. and Ray, S. (2007), "Artificial neural network modeling of creep behavior in a rotating composite disc", Eng. Computation., 24(2), 151-164.   DOI
11 Gupta, R.K., Patel, K.A., Chaudhary, S. and Nagpal, A.K. (2013), "Closed form solution for deflection of flexible composite bridges", Procedia Eng., 51, 75-83.   DOI
12 Hsu, D.S., Yeh, I.C. and Lian, W.T. (1993), "Artificial neural damage detection of existing structure", Proceedings of the 3rd ROC and Japan Seminar on Natural Hazards Mitigation, Tainan, Taiwan, November, pp. 423-436.
13 Kanwar, V., Kwatra, N. and Aggarwal, P. (2007), "Damage detection for framed RCC buildings using ANN modeling", Int. J. Damage Mech., 16(4), 457-472.   DOI
14 Kaloop, M.R. and Kim, D. (2014), "GPS-structural health monitoring of a long span bridge using neural network adaptive filter", Survey Review, 16(334), 7-14.
15 Kawamura, K., Miyamoto, A., Frangopol, D.M. and Abe, M. (2004), "Performance evaluation system for main reinforced concrete girders of existing bridges", Transport. Res. Rec., 1866, 67-78.   DOI
16 Kim, D.K., Kim, D.H., Cui, J., Seo, H.Y. and Lee, Y.H. (2009), "Iterative neural network strategy for static model identification of an FRP deck", Steel Comp. Struct., Int. J., 9(5), 445-455.   DOI
17 Kwatra, N., Godbole, P.N. and Krishna, P. (2002), "Application of artificial neural network for determination of wind induced pressures on gable roof", Wind Struct., 5(1), 1-14.   DOI
18 Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, M.Z., Jameel, M., Hakim, S.J.S. and Zargar, M. (2013b), "Application of the ANFIS model in deflection prediction of concrete deep beam", Struc. Eng. Mech., Int. J., 45(3), 319-332.
19 Min, J., Park, S., Yun, C.B., Lee, C.G. and Lee, C. (2012), "Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity", Eng. Struct., 39, 210-220.   DOI
20 Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, M.Z., Jameel, M. and Arumugam, A.M.S. (2013a), "Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams", Comput. Concrete, 11(3), 237-252.   DOI   ScienceOn
21 Pendharkar, U., Chaudhary, S. and Nagpal, A.K. (2007), "Neural network for bending moment in continuous composite beams considering cracking and time effects in concrete", Eng. Struct., 29(9), 2069-2079.   DOI
22 Pendharkar, U., Chaudhary, S. and Nagpal, A.K. (2010), "Neural networks for inelastic mid-span deflections in continuous composite beams", Struc. Eng. Mech., Int. J., 36(2), 165-179.   DOI   ScienceOn
23 Pendharkar, U., Chaudhary, S. and Nagpal, A.K. (2011), "Prediction of moments in composite frames considering cracking and time effects using neural network models", Struc. Eng. Mech., Int. J., 39(2), 267-285.   DOI   ScienceOn
24 Reich, Y. and Barai, S.V. (1999), "Evaluating machine learning models for engineering problems", Artif. Intell. Eng., 13(3), 257-272.   DOI
25 Shahin, M. and Elchanakani, M. (2008), "Neural networks for ultimate pure bending of steel circular tubes", J. Constr. Steel Res., 64(6), 624-633.   DOI
26 Uddin, M.A., Jameel, M., Razak, H.A. and Islam, A.B.M. (2012), "Response prediction of offshore floating structure using artificial neural network", Adv. Sci. Lett., 14(1), 186-189.   DOI
27 Sharma, R.K., Maru, S. and Nagpal, A.K. (2003), "Effect of creep and shrinkage in a class of composite frame-shear wall systems", Steel Comp. Struct., Int. J., 3(5), 333-348.   DOI
28 Sttutgart Neural Network Simulator (SNNS) user manual (1998), University of Sttutgart: Institute For Parallel and Distributed High Performance Systems (IPVR), Version 4.2, Accessed on December 27, 2012; Available at: http://www-ra.informatik.uni-tuebingen.de/SNNS/
29 Tadesse, Z., Patel, K.A., Chaudhary, S. and Nagpal, A.K. (2012), "Neural networks for prediction of deflection in composite bridges", J. Constr. Steel Res., 68(1), 138-149.   DOI
30 Wang, W.W., Dai, J.G., Guo, L. and Huang, C.K. (2011), "Long-term behavior of prestressed old-new concrete composites beams", J. Bridge Eng., 16(2), 275-285.   DOI   ScienceOn