DOI QR코드

DOI QR Code

Torsional parameters importance in the structural response of multiscale asymmetric-plan buildings

  • Received : 2016.05.23
  • Accepted : 2016.07.15
  • Published : 2017.03.25

Abstract

The evaluation of torsional effects on multistory buildings remains an open issue, despite considerable research efforts and numerous publications. In this study, a large number of multiple test structures are considered with normally distributed topological attributes, in order to quantify the statistically derived relationships between the torsional criteria and response parameters. The linear regression analysis results, depict that the center of twist and the ratio of torsion (ROT) index proved numerically to be the most reliable criteria for the prediction of the modal rotation and displacements, however the residuals distribution and R-squared derived for the ductility demands prediction, was not constant and low respectively. Thus, the assessment of the torsional parameters' contribution to the nonlinear structural response was investigated using artificial neural networks. Utilizing the connection weights approach, the Center of Strength, Torsional Stiffness and the Base Shear Torque curves were found to exhibit the highest impact numerically, while all the other torsional indices' contribution was investigated and quantified.

Keywords

References

  1. Anagnostopoulos, S.A., Kyrkos, M.T. and Stathopoulos, K.G. (2015), "Earthquake induced torsion in buildings: Critical review and state of the art", Earthq. Struct., 8(2), 305-377. https://doi.org/10.12989/eas.2015.8.2.305
  2. Beycioglu, A., Emiroglu, M., Kocak, Y. and Subasi, S. (2015), "Analyzing the compressive strength of clinker mortars using approximate reasoning approaches-ANN vs MLR", Comput. Concrete, 15(1), 89-101. https://doi.org/10.12989/cac.2015.15.1.089
  3. Box, G.E. and Muller, M.E. (1958), "A note on the generation of random normal deviates", Ann. Math. Stat., 29(2), 610-611. https://doi.org/10.1214/aoms/1177706645
  4. De La Llera, J.C. and Chopra, A.K. (1994), "Accidental and natural torsion in earthquake response and design of buildings", Earthquake Engineering Research Center, University of California, Berkeley, U.S.A.
  5. De Llera, J.C.L. and Chopra, A.K. (1995), "A simplified model for analysis and design of asymmetric‐plan buildings", Earthq. Eng. Struct. Dyn., 24(4), 573-594. https://doi.org/10.1002/eqe.4290240408
  6. De Llera, J.C.L. and Chopra, A.K. (1995), "Understanding the inelastic seismic behaviour of asymmetric‐plan buildings", Earthq. Eng. Struct. Dyn., 24(4), 549-572. https://doi.org/10.1002/eqe.4290240407
  7. Gevrey, M., Dimopoulos, I. and Lek, S. (2003), "Review and comparison of methods to study the contribution of variables in artificial neural network models", Ecol. Model., 160(3), 249-264. https://doi.org/10.1016/S0304-3800(02)00257-0
  8. Glantz, S.A. and Bryan, K.S. (1990), Primer of Applied Regression and Analysis of Variance, McGraw-Hill, New York, U.S.A.
  9. Hakim, S.J.S. and Razak, H.A. (2013), "Adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for structural damage identification", Struct. Eng. Mech., 45(6), 779-802. https://doi.org/10.12989/sem.2013.45.6.779
  10. Hejal, R. and Chopra, A.K. (1987), "Earthquake response of torsionally-coupled buildings", Earthquake Engineering Research Center, University of California, Berkeley, U.S.A.
  11. Hejal, R. and Chopra, A.K. (1987), "Earthquake response of torsionally-coupled buildings", Earthquake Engineering Research Center, University of California, Berkeley, U.S.A.
  12. Humar, J.M. and Fazileh, F. (2010), "Discussion of „seismic behavior of a single‐story asymmetric‐plan buildings under uniaxial excitation", Earthq. Eng. Struct. Dyn., 39(6), 705-708. https://doi.org/10.1002/eqe.969
  13. Inaudi, J.A. and De La Llera, J.C. (1992), "Dynamic analysis of nonlinear structures using state-space formulation and partitioned integration schemes", University of California, Berkeley, U.S.A.
  14. Lagaros, N.D., Bakas, N. and Papadrakakis, M. (2009), "Optimum design approaches for improving the seismic performance of 3D RC buildings", J. Earthq. Eng., 13(3), 345-363. https://doi.org/10.1080/13632460802598594
  15. Lagaros, N.D., Papadrakakis, M. and Bakas, N. (2006), "Automatic minimization of the rigidity eccentricity of 3D reinforced concrete buildings", J. Earthq. Eng., 10(4), 533-564. https://doi.org/10.1080/13632460609350609
  16. Lawrence, S.C., and Lee, G. and Chung, T.A. (1997), "Lessons in neural network training: Overfitting may be harder than expected", Proceedings of the Ninth Innovative Applications of Artificial Intelligence Conference on Artificial Intelligence.
  17. Li, P.H., Zhu, H.P., Luo, H. and Weng, S. (2015), "Structural damage identification based on genetically trained ANNs in beams", Smart Struct. Syst., 15(1), 227-244. https://doi.org/10.12989/sss.2015.15.1.227
  18. Llera, J.C.L.D. and Chopra, A.K. (1995), "Understanding the inelastic seismic behaviour of asymmetricplan buildings", Earthq. Eng. Struct. Dyn., 24(4), 549-572. https://doi.org/10.1002/eqe.4290240407
  19. Lucchini, A., Monti, G. and Kunnath, S. (2010), "Nonlinear response of two-way asymmetric single-story building under biaxial excitation", J. Struct. Eng., 137(1), 34-40.
  20. Makridakis, S., Steven, C., Wheelwright, S.C. and Hyndman, R.J. (2008), Forecasting Methods and Applications, John Wiley & Sons, U.S.A.
  21. Marquardt, D.W. (1963), "An algorithm for least-squares estimation of nonlinear parameters", J. Soc. Ind. Appl. Math., 11(2), 431-441. https://doi.org/10.1137/0111030
  22. Mohammadhassani, M., Nezamabadi-pour M., Suhatril, M. and Shariati, M. (2013), "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. Myslimaj, B. and Tso, W.K. (2002), "A strength distribution criterion for minimizing torsional response of asymmetric wall‐type systems", Earthq. Eng. Struct. Dyn., 31(1), 99-120. https://doi.org/10.1002/eqe.100
  24. Olden, J.D. and Jackson, D.A. (2002), "Illuminating the "black box": A randomization approach for understanding variable contributions in artificial neural networks", Ecol. Model., 154(1), 135-150. https://doi.org/10.1016/S0304-3800(02)00064-9
  25. Olden, J.D., Joy, M.K. and Death, R.G. (2004), "An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data", Ecol. Model., 178(3), 389-397. https://doi.org/10.1016/j.ecolmodel.2004.03.013
  26. Paulay, T. (1997), "Displacement-based design approach to earthquake-induced torsion in ductile buildings", Eng. Struct., 19(9), 699-707. https://doi.org/10.1016/S0141-0296(97)00167-3
  27. Paulay, T. (1998), "Torsional mechanisms in ductile building systems", Earthq. Eng. Struct. Dyn., 27(10), 1101-1121. https://doi.org/10.1002/(SICI)1096-9845(199810)27:10<1101::AID-EQE773>3.0.CO;2-9
  28. Rojas, R. (2013), Neural Networks: A Systematic Introduction, Springer Science & Business Media.
  29. Stathi, C.G., Bakas, N.P., Lagaros, N.D. and Papadrakakis, M. (2015), Ratio of Torsion (ROT): An Index.
  30. Tavakkol, S., Alapour, F., Kazemian, A., Hasaninejad, A., Ghanbari, A. and Ramezanianpour, A.A. (2013), "Prediction of lightweight concrete strength by categorized regression, MLR and ANN", Comput. Concrete, 12(2), 151-167. https://doi.org/10.12989/cac.2013.12.2.151
  31. Yavuz, G. (2016), "Shear strength estimation of RC deep beams using the ANN and strut-and-tie approaches", Struct. Eng. Mech., 57(4), 657-680. https://doi.org/10.12989/sem.2016.57.4.657