A methodology has been developed for the continuous composite beams to predict the inelastic bending moments (considering the cracking of concrete) from the elastic moments (neglecting the cracking) by using the neural networks. The proposed neural network models predict the inelastic moment ratios (ratio of inelastic moment to elastic moment) at the supports of a span. Nine significant structural parameters have been identified governing the inelastic moment ratios. Six neural networks have been presented to cover the entire practical range of the beams. The proposed neural networks have been validated for a number of beams with different number of spans and the errors are shown to be small for practical purposes. The methodology enables rapid estimation of inelastic moments. The methodology can easily be extended for large composite building frames where a very significant saving in computational effort would result. The feasibility of the methodology for building frames has been demonstrated by considering a single story frame.
BradfordM.A.ManhH.V. and GilbertR.I. (2002). “Numerical analysis of continuous composite beams at service loading”, Advances in Structural Engineering, Vol. 5, No. 1, pp. 1–12.
2.
CEB-FIP Model Code 1990 (1993). Bulletin d' information No. 213/214, Laussane (Switzerland).
3.
FengQ.M.KimD.K.YiJ.H. and ChenY. (2004). “Baseline models for bridge performance monitoring”, Journal of Engineering Mechanics, ASCE, Vol. 130, No. 5, pp. 562–569.
4.
GhaliA.FavreR. and ElbadryM. (2002). Concrete Structures: Stresses and Deformations, 3rd Ed., Spon Press, London.
5.
GilbertR.I. and BradfordM.A. (1995). “Time-dependent behavior of composite beams at service loads”, Journal of Structural Engineering, ASCE, Vol. 121, No. 2, pp. 319–327.
6.
HadiM.N.S. (2003). “Neural network applications in concrete structures”, Computers and Structures, Vol. 81, No. 6, pp. 373–381.
7.
HajelaP. and BerkeL. (1991). “Neurobiological computational models in structural analysis and design”, Computers and Structures, Vol. 41, No. 4, pp. 657–667.
8.
HajelaP. and BerkeL. (1992). “Neural networks in engineering analysis and design”, Computing Systems in Engineering, Vol. 3, No. 1, pp. 525–538.
9.
HsuD.S.YehI.C. and LianW.T. (1993). “Artificial neural damage detection of existing structure”, Proceedings of 3rd ROC and Japan Seminar on Natural Hazards Mitigation, Tainan, pp. 423–436
10.
JengC.H. and MoY.L. (2004). “Quick seismic response estimation of prestressed concrete bridges using artificial neural networks”, Journal of Computing in Civil Engineering, ASCE, Vol. 18, No. 4, pp. 360–369.
11.
JenkinsW.M. (2002). “Structural reanalysis using a neural network-based iterative method”, Journal of Structural Engineering, ASCE, Vol. 128, No. 7, pp. 946–950.
12.
JiangS.F.ZhangC.M. and KohC.G. (2006). “Structural damage detection by integrating data fusion and probabilistic neural network”, Advances in Structural Engineering, Vol. 9, No. 4, pp. 445–458.
13.
KawamuraK.MiyamotoA.FrangopolD.M. and RiyuichiK. (2003). “Performance evaluation of concrete slabs of existing bridges using neural networks”, Engineering Structures, Vol. 25, No. 12, pp. 1455–1477.
14.
KawamuraK.MiyamotoA.FrangopolD.M. and AbeM. (2004). “Performance evaluation system for main reinforced concrete girders of existing bridges”, Transportation Research Record, pp. 67–78.
15.
KimJ.I.KimD.K.FengM.Q. and YazdaniF. (2004). “Application of neural networks for estimation of concrete strength”, Journal of Materials in Civil Engineering, ASCE, Vol. 16, No. 3, pp. 257–264.
16.
KimD.K.LeeJ.J.LeeJ.H. and ChangS.K. (2005). “Application of probabilistic neural networks for prediction of concrete strength”, Journal of Materials in Civil Engineering, ASCE, Vol. 17, No. 3, pp. 353–362.
17.
LeeS.C. (2003). “Prediction of concrete strength using artificial neural networks”, Engineering Structures, Vol. 25, No. 7, pp. 849–857.
18.
LeeJ.J.LeeJ.W.YiJ.H.YunC.B. and JungH.Y. (2005). “Neural network-based damage detection for bridges considering errors in baseline finite element models”, Journal of Sound and Vibration, Vol. 280, pp. 555–578.
19.
MaruS. and NagpalA.K. (2004). “Neural network for creep and shrinkage deflections in reinforced concrete frames”, Journal of Computing in Civil Engineering, ASCE, Vol. 18, No. 4, pp. 350–359.
20.
MoY.L. and LinS.S. (1994). “Investigation of framed shearwall behavior with neural networks”, Magazine of Concrete Research, Vol. 46, No. 169, pp. 289–300.
21.
MoY.L. and HanR.H. (1995). “Investigation of prestressed concrete frame behavior with neural networks”, Journal of Intelligent Material Systems and Structures, Vol. 6, pp. 566–573.
22.
MoY.L. and KoanK.J. (1998). “Investigation of welding effect on rebars using neural networks”, Journal of Testing and Evaluation, ASTM, Vol. 26, No. 3, pp. 285–292.
23.
MoY.L.HungH.Y. and ZhongJ. (2002). “Investigation of stress-strain relationship of confined concrete in hollow bridge columns using neural networks”, Journal of Testing and Evaluation, ASTM, Vol. 30, No. 4, pp. 330–339.
24.
OretaA.W.C. and KawashimaK. (2003). “Neural network modeling of confined compressive strength and strain of circular concrete columns”, Journal of Structural Engineering, ASCE, Vol. 129, No. 4, pp. 554–561.
25.
OretaA.W.C. (2004). “Simulating size effect on shear strength design of RC beams without stirrups using neural networks”, Engineering Structures, Vol. 26, No. 5, pp. 681–691.
26.
SaklaS.S.S. (2004). “Neural network modeling of the load-carrying capacity of eccentrically- loaded single-angle struts”, Journal of Constructional Steel Research, Vol. 60, No. 7, pp. 965–987.
SircaG.F. and AdeliH. (2004). “Counterpropagation neural network model for steel girder bridge structures”, Journal of Bridge Engineering, ASCE, Vol. 9, No. 1, pp. 55–65.
29.
YeungW.T. and SmithJ.W. (2005). “Damage detection in bridges using neural networks for pattern recognition of vibration signatures”, Engineering Structures, Vol. 27, No. 5, pp. 685–698.
