Structural Engineering and Mechanics

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Explicit expressions for inelastic design quantities in composite frames considering effects of nearby columns and floors

  • Ramnavas, M.P. (Department of Civil Engineering, Indian Institute of Technology Delhi) ;
  • Patel, K.A. (Department of Civil Engineering, Institute of Infrastructure, Technology, Research And Management (IITRAM)) ;
  • Chaudhary, Sandeep (Discipline of Civil Engineering, Indian Institute of Technology Indore) ;
  • Nagpal, A.K. (Department of Civil Engineering, Indian Institute of Technology Delhi)
  • Received : 2017.01.02
  • Accepted : 2017.07.10
  • Published : 2017.11.25
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Abstract

Explicit expressions for rapid prediction of inelastic design quantities (considering cracking of concrete) from corresponding elastic quantities, are presented for multi-storey composite frames (with steel columns and steel-concrete composite beams) subjected to service load. These expressions have been developed from weights and biases of the trained neural networks considering concrete stress, relative stiffness of beams and columns including effects of cracking in the floors below and above. Large amount of data sets required for training of neural networks have been generated using an analytical-numerical procedure developed by the authors. The neural networks have been developed for moments and deflections, for first floor, intermediate floors (second floor to ante-penultimate floor), penultimate floor and topmost floor. In the case of moments, expressions have been proposed for exterior end of exterior beam, interior end of exterior beam and both interior ends of interior beams, for each type of floor with a total of twelve expressions. Similarly, in the case of deflections, expressions have been proposed for exterior beam and interior beam of each type of floor with a total of eight expressions. The proposed expressions have been verified by comparison of the results with those obtained from the analytical-numerical procedure. This methodology helps to obtain the inelastic design quantities from the elastic quantities with simple calculations and thus would be very useful in preliminary design.

Keywords

References

  1. Baskar, K., Shanmugam, N.E. and Thevendran, V. (2002), "Finite-element analysis of steel-concrete composite plate girder", J. Struct. Eng., 128(9), 1158-1168. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:9(1158)
  2. Bigdeli, Y. and Kim, D. (2017), "Development of energy based Neuro-Wavelet algorithm to suppress structural vibration", Struct. Eng. Mech., 62(2), 237-246. https://doi.org/10.12989/sem.2017.62.2.237
  3. Bigdeli, Y., Kim, D.K. and Chang, S. (2014), "Vibration control of 3D irregular buildings by using developed neuro-controller strategy", Struct. Eng. Mech., 49(6), 687-703. https://doi.org/10.12989/sem.2014.49.6.687
  4. Chandak, R., Upadhyay, A. and Bhargava, P. (2008), "Shear lag prediction in symmetrical laminated composite box beams using artificial neural network", Struct. Eng. Mech., 29(1), 77-89. https://doi.org/10.12989/sem.2008.29.1.077
  5. Chaudhary, S., Pendharkar, U. and Nagpal, A.K. (2007), "Hybrid procedure for cracking and time-dependent effects in composite frames at service load", J. Struct. Eng., 133(2), 166-175. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:2(166)
  6. Costa-Neves, L.F., da Silva, J.G.S., de Lima, L.R.O. and Jordao, S. (2014), "Multi-storey, multi-bay buildings with composite steel-deck floors under human-induced loads: The human comfort issue", Comput. Struct., 136, 34-46. https://doi.org/10.1016/j.compstruc.2014.01.027
  7. Dai, J.G., Ueda, T., Sato, Y. and Nagai, K. (2012), "Modeling of tension stiffening behavior in FRP-strengthened RC members based on rigid body spring networks", Comput. Aid. Civil Infrastruct. Eng., 27(6), 406-418. https://doi.org/10.1111/j.1467-8667.2011.00741.x
  8. Gedam, B.A., Bhandari, N.M. and Upadhyay, A. (2014), "An apt material model for drying shrinkage and specific creep of HPC using artificial neural network", Struct. Eng. Mech., 52(1), 97-113. https://doi.org/10.12989/sem.2014.52.1.097
  9. Gupta, R.K., Kumar, S., Patel, K.A., Chaudhary, S. and Nagpal, A.K. (2015), "Rapid prediction of deflections in multi-span continuous composite bridges using neural networks", Int. J. Steel Struct., 15(4), 893-909. https://doi.org/10.1007/s13296-015-1211-9
  10. He, J., Liu, Y., Chen, A. and Yoda, T. (2010), "Experimental study on inelastic mechanical behaviour of composite girders under hogging moment", J. Constr. Steel Res., 66(1), 37-52. https://doi.org/10.1016/j.jcsr.2009.07.005
  11. Joshi, S.G., Londhe, S.N. and Kwatra, N. (2014), "Application of artificial neural networks for dynamic analysis of building frames", Comput. Concrete, 13(6), 765-780. https://doi.org/10.12989/cac.2014.13.6.765
  12. Kaloop, M.R. and Kim, D.K. (2014), "GPS-structural health monitoring of a long span bridge using neural network adaptive filter", Surv. Rev., 16(334), 7-14.
  13. Kaloop, M.R., Sayed, M.A., Kim, D.K. and Kim, E. (2014), "Movement identification model of port container crane based on structural health monitoring system", Struct. Eng. Mech., 50(1), 105-119. https://doi.org/10.12989/sem.2014.50.1.105
  14. Khan, M.I. (2012), "Predicting properties of high performance concrete containing composite cementitious materials using artificial neural networks", Automat. Constr., 22, 516-524. https://doi.org/10.1016/j.autcon.2011.11.011
  15. Kim, D.H. and Kim, D. K. (2009), "Application of lattice probabilistic neural network for active response control of offshore structures", Struct. Eng. Mech., 31(2), 153-162. https://doi.org/10.12989/sem.2009.31.2.153
  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 Compos. Struct., 9(5), 445-455. https://doi.org/10.12989/scs.2009.9.5.445
  17. Mallela, U.K. and Upadhyay, A. (2016), "Buckling load prediction of laminated composite stiffened panels subjected to in-plane shear using artificial neural networks", Thin Wall Struct., 102, 158-164. https://doi.org/10.1016/j.tws.2016.01.025
  18. Maru, S. and Nagpal, A.K. (2004), "Neural network for creep and shrinkage deflections in reinforced concrete frames", J. Comput. Civil Eng., 18(4), 350-359. https://doi.org/10.1061/(ASCE)0887-3801(2004)18:4(350)
  19. MATLAB (2009), Neural Networks Toolbox User's Guide, The Mathworks Inc., USA.
  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. https://doi.org/10.12989/cac.2013.11.3.237
  21. 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", Struct. Eng. Mech., 45(3), 319-332.
  22. Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M. and Shariati, M. (2013c), "Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams", Struct. Eng. Mech., 46(6), 853-868. https://doi.org/10.12989/sem.2013.46.6.853
  23. Panigrahi, R., Gupta, A. and Bhalla, S. (2008), "Design of tensegrity structures using artificial neural networks", Struct. Eng. Mech., 29(2), 223-235. https://doi.org/10.12989/sem.2008.29.2.223
  24. Patel, K.A., Chaudhary, S. and Nagpal, A.K. (2016), "A tension stiffening model for analysis of reinforced concrete flexural members subjected to service load", Comput. Concrete, 17(1), 29-51. https://doi.org/10.12989/cac.2016.17.1.029
  25. Pendharkar, U., Chaudhary, S. and Nagpal, A.K. (2010), "Neural networks for inelastic mid-span deflections in continuous composite beams", Struct. Eng. Mech., 36(2), 165-179. https://doi.org/10.12989/sem.2010.36.2.165
  26. Pendharkar, U., Chaudhary, S. and Nagpal, A.K. (2011), "Prediction of moments in composite frames considering cracking and time effects using neural network models", Struct. Eng. Mech., 39(2), 267-285. https://doi.org/10.12989/sem.2011.39.2.267
  27. Pendharkar, U., Patel, K.A., Chaudhary, S. and Nagpal, A.K. (2015), "Rapid prediction of long-term deflections in composite frames", Steel Compos. Struct., 18(3), 547-563. https://doi.org/10.12989/scs.2015.18.3.547
  28. Ramnavas, M.P. (2016), "Development of computational techniques for service load analysis of steel-concrete composite structures", Ph.D. Thesis, Indian Institute of Technology Delhi, New Delhi.
  29. Ramnavas, M.P., Patel, K.A., Chaudhary, S. and Nagpal, A.K. (2017), "Service load analysis of composite frames using cracked span length frame element", Eng. Struct., 132, 733-744. https://doi.org/10.1016/j.engstruct.2016.11.071
  30. Sahamitmongkol, R. and Kishi, T. (2011), "Tension stiffening effect and bonding characteristics of chemically prestressed concrete under tension", Mater. Struct., 44(2), 455-474. https://doi.org/10.1617/s11527-010-9641-5
  31. 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. https://doi.org/10.1016/j.jcsr.2011.08.003
  32. Varshney, L.K., Patel, K.A., Chaudhary, S. and Nagpal, A.K. (2013), "Control of time-dependent effects in steel-concrete composite frames", Int. J. Steel Struct., 13(4), 589-606. https://doi.org/10.1007/s13296-013-4002-1
  33. Zona, A., Barbato, M. and Conte, J.P. (2008), "Nonlinear seismic response analysis of steel-concrete composite frames", J. Struct. Eng., 134(6), 986-997. https://doi.org/10.1061/(ASCE)0733-9445(2008)134:6(986)