Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Effects of infill walls on RC buildings under time history loading using genetic programming and neuro-fuzzy

  • Kose, M. Metin (Department of Civil Engineering, Faculty of Engineering & Architecture, K. Sutcu Imam University) ;
  • Kayadelen, Cafer (Department of Civil Engineering, Faculty of Engineering & Architecture, K. Sutcu Imam University)
  • Received : 2012.08.24
  • Accepted : 2013.07.31
  • Published : 2013.08.10
⟨ Previous Next ⟩

Abstract

In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.

Keywords

References

  1. Anil, O. and Altin, S. (2007), "An experimental study on reinforced concrete partially infilled frames", Eng. Struct., 29(3), 449-60. https://doi.org/10.1016/j.engstruct.2006.05.011
  2. Cavaleri, L. and Papia, M. (2003), "A new dynamic identification technique: Application to the evaluation of the equivalent strut for infilled frames", Eng. Struct., 25(7), 889-901. https://doi.org/10.1016/S0141-0296(03)00023-3
  3. CSI (2006), Integrated Structural Analysis & Design Software, Computers and Structures Inc., Berkeley, CA.
  4. Cevik, A. (2007), "A new formulation for longitudinally stiffened webs subjected to patch loading", J. Construct Steel Res., 63, 1328-1340. https://doi.org/10.1016/j.jcsr.2006.12.004
  5. Darus, I.Z.M. and Al-Khafaji, A.A.M. (2012), "Non-parametric modelling of a rectangular flexible plate structure", Eng. Appl. Artif. Intel., 25(1), 94-106. https://doi.org/10.1016/j.engappai.2011.09.009
  6. Demuth, H. and Beale, M. (2001), Neural network toolbox for use with MATLAB, The MathWorks Inc., Natick, MA.
  7. Dubois, D. and Prade, H. (1980), Fuzzy sets and systems - Theory and applications, Academic press, New York.
  8. Eurocode 8 (2003), Design of Structures for Earthquakes Resistance-Part 1: General Rules, Seismic Actions and Rules for Buildings, Pr-EN 1998-1 Final Draft, Comite Europeen de Normalisation.
  9. Ferreira, C. (2001), "Gene expression programming: a new adaptive algorithm for solving problems", Complex Syst., 13(2), 87-129.
  10. Fonseca, E.T., Vellasco, P.C.G. da S., Andrade, S.A.L. de and Vellasco, M.M.B.R. (2003), "Neural network evaluation of steel beam patch load capacity", Adv. Eng. Softw., 34(11-12), 763-772. https://doi.org/10.1016/S0965-9978(03)00104-2
  11. Jang, J.S.R. (1993), "ANFIS: adaptive-network-based fuzzy inference systems", IEEE Trans Syst. Man. Cybern., 23(3), 665-685. https://doi.org/10.1109/21.256541
  12. Kose, M.M. and Karslioglu, O. (2011), "Effects of infill walls on base responses and roof drift of reinforced concrete buildings under time-history loading", Struct. Des. Tall Spec. Build., 20, 402-417. https://doi.org/10.1002/tal.535
  13. Munoz, D.G. (2005), "Discovering unknown equations that describe large data sets using genetic programming techniques", M.S. Thesis, Linkoping Institute of Technology.
  14. Padmini, D., Ilamparuthi, K. and Sudheer, K.P. (2008), "Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models", Comput. Geotech., 35, 33-46. https://doi.org/10.1016/j.compgeo.2007.03.001
  15. Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2003), "Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models", Comput Geotech., 30, 637-647. https://doi.org/10.1016/j.compgeo.2003.09.004
  16. Sherrwood, P.H. (2008), DTREG Predictive Modeling Software, http://www. dtreg.com.
  17. Stemberk, P., da Silva, W.R.L., Sykorova, J. and Bartova, J. (2013), "Fuzzy modeling of combined effect of winter road maintenance and cyclic loading on concrete slab bridge", Adv. Eng. Softw., 62-63, 97-108. https://doi.org/10.1016/j.advengsoft.2013.04.008
  18. Takagi, T. and Sugeno, M. (1985), "Fuzzy identification of systems and its applications to modeling and control", IEEE Trans. Syst. Man. Cybern., 15, 116-132.
  19. Teodorescu, L., Sherwood, D. (2008), "High energy physics event selection with gene expression programming", Comput Phys Commun., 178, 409-19. https://doi.org/10.1016/j.cpc.2007.10.003
  20. Topcu, I.B. and Saridemir, M. (2008), "Prediction of rubberized concrete properties using artificial neural network and fuzzy logic", Constr. Build. Mater., 22, 532-540. https://doi.org/10.1016/j.conbuildmat.2006.11.007
  21. Tutmez, B. and Tercan, A.E. (2007), "Spatial estimation of some mechanical properties of rocks by fuzzy modeling", Comput. Geotech., 34,10-18. https://doi.org/10.1016/j.compgeo.2006.09.005
  22. UBC (1997), Uniform Building Code, International Conference of Building Officials, Whittier, CA.
  23. Vieira, J., Dias, F.M. and Mota, A. (2004), "Artificial neural networks and neuro-fuzzy systems for modelling and controlling real systems: a comparative study", Eng. Appl. Artif. Intel., 17(3), 265-273. https://doi.org/10.1016/j.engappai.2004.03.001
  24. Villaverde, R. (2006), "Simple method to estimate the seismic nonlinear response of nonstructural; components in buildings", Eng. Struct., 28, 1450-1461. https://doi.org/10.1016/j.engstruct.2005.12.016
  25. Zadeh, L.A. (1965), "Fuzzy sets", Inform. Control, 8, 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  26. Zheng, S.J., Li, Z.Q. and Wang, H.T. (2011), "A genetic fuzzy radial basis function neural network for structural health monitoring of composite laminated beams", Exp. Syst. Appl., 38(9), 11837-11842. https://doi.org/10.1016/j.eswa.2011.03.072

Cited by

  1. Parameters affecting the fundamental period of infilled RC frame structures vol.9, pp.5, 2015, https://doi.org/10.12989/eas.2015.9.5.999
  2. Neuro-fuzzy and artificial neural networks modeling of uniform temperature effects of symmetric parabolic haunched beams vol.56, pp.5, 2015, https://doi.org/10.12989/sem.2015.56.5.787
  3. Evaluation of the seismic performance of special moment frames using incremental nonlinear dynamic analysis vol.63, pp.2, 2013, https://doi.org/10.12989/sem.2017.63.2.259
  4. Discharge coefficient estimation for rectangular side weir using GEP and GMDH methods vol.6, pp.2, 2013, https://doi.org/10.12989/acd.2021.6.2.135