Structural Engineering and Mechanics

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Estimation of fundamental period of reinforced concrete shear wall buildings using self organization feature map

  • Received : 2016.08.22
  • Accepted : 2017.05.15
  • Published : 2017.07.25
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Abstract

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.

Keywords

References

  1. Aguilar, V., Sandoval, C., Adam, J.M., Garzon-Roca, J. and Valdebenito, G. (2016), "Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural network", Struct. Infrastruct. Eng., 12(12), 1663-1676.
  2. Ahmadi, M. and Bakar, S.A. (2014), "Shear wall effect on material consumption based on seismic design", Gradevinar, 66(1), 1-9.
  3. Amanat, K.M. and Hoque, E. (2006), "A rationale for determining the natural period of RC building frames having infill", Eng. Struct., 28(4), 495-502. https://doi.org/10.1016/j.engstruct.2005.09.004
  4. Asteris, P.A., Repapis, C.C., Cavaleri, L., Sarhosis, V. and Athanasopoulou, A. (2015), "On the fundamental period of infilled RC frame buildings", Struct. Eng. Mech., 54(6), 1175-1200. https://doi.org/10.12989/sem.2015.54.6.1175
  5. Asteris, P.A., Repapis, C.C., Foskolos, F., Fotos, A. and Tsaris, A.K. (2017), "Fundamental period of infilled RC frame structures with vertical irregularity", Struct. Eng. Mech., 61(5), 663-674. https://doi.org/10.12989/sem.2017.61.5.663
  6. Asteris, P.G., Tsaris, A.K., Cavaleri, L., Repapis, C.C., Papalou, A., Di Trapani, F. and Karypidis, D.F. (2016), "Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks", Comput. Intell. Neurosci., Volume 2016, Article ID 5104907, 1-12.
  7. ATC (1978), Tentative provisions for the development of seismic regulations for buildings, Report No. ATC3-06, Applied Technology Council, Palo Alto, California.
  8. Chandwani, V., Agrawal, V. and Nagar, R. (2015), "Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks", Expert Syst. Appl., 42(2), 885-893. https://doi.org/10.1016/j.eswa.2014.08.048
  9. Cole, E.E., Tokas, C.V. and Meehan, J.F. (1992), "Analysis of recorded building data to verify or improve 1991 Uniform Building Code (UBC) period of vibration formulas", Proceedings of SMIP92, Strong Motion Instrumentation Program, Divisions of Mines and Geology, California Department of Conservation, Sacramento.
  10. Costa Rican Seismic Code (1986), Seismic Code of Costa Rica, Federal College of Engineers and Architects of Costa Rica, San Jose, Costa Rica.
  11. E.A.K. (2003), Greek Earthquake Resistant Design Code, Earthquake Design and Protection Organization (OASP) and Technical Chamber of Greece (TEE), Athens.
  12. Egyptian Seismic Code (1988), Regulations for Earthquake Resistant Design of Buildings in Egypt, Egyptian Society for Earthquake Engineering, Cairo, Egypt.
  13. Ellis, B.R. (1980), "An assessment of the accuracy of predicting the fundamental natural frequencies of building and the implications concerning the dynamic analysis of structures", Proceedings of the Institution of Civil Engineers, 69(2), 763-776. https://doi.org/10.1680/iicep.1980.2376
  14. EN 1998-1:2004 (2004), Eurocode 8: design of structures for earthquake resistance-part 1: general rules, seismic actions and rules for buildings, European Committee for Standardization.
  15. FEMA-450 (2003), NEHRP recommended provisions for seismic regulations for new buildings and other structures, Part 1: Provisions, Washington (DC), Federal Emergency Management Agency.
  16. Gallipoli, M.R., Mucciarelli, M., Sket-Motnikar, B., Zupancic, P., Gosar, A., Prevolnik, S., Herak, M., Stipcevic, J., Herak, D., Milutinovic, Z. and Olumceva, T. (2010), "Empirical estimates of dynamic parameters on a large set of European buildings", Bull. Earthq. Eng., 8, 593-607. https://doi.org/10.1007/s10518-009-9133-6
  17. Gavin, J. and Bowden, G.C. (2005), "Input determination for neural network models in water resources applications. Part 1-background and methodology", J. Hydrol., 301(1-4), 75-92. https://doi.org/10.1016/j.jhydrol.2004.06.021
  18. Gilles, D. (2010), "In situ dynamic properties of buildings in Montreal determined from ambient vibration records", PhD Thesis, Department of Civil Engineering and Applied Mechanics, McGill University Montreal.
  19. Gilles, D. and McClure, G. (2008), "Development of a period database for buildings in Montreal using ambient vibrations", Proceedings of the 14th WCEE, Beijing, China.
  20. Goel, R.K. and Chopra, A.K. (1998), "Period formulas for concrete shear wall buildings", J. Struct. Eng., 124(4), 426-433. https://doi.org/10.1061/(ASCE)0733-9445(1998)124:4(426)
  21. Gonzales, H. and Lopez-Almansa, F. (2012), "Seismic performance of buildings with thin RC bearing walls", Eng. Struct., 34, 244-258. https://doi.org/10.1016/j.engstruct.2011.10.007
  22. Gutta, S., Philomin, V. and Trajkovic, M. (2005), "Self-organizing feature map with improved performance by non-monotonic variation of the learning rate", Free Patents Online (FPO).
  23. Hadzima-Nyarko, M., Moric, D., Draganic, H. and Stefic, T. (2015), "Comparison of fundamental periods of reinforced shear wall dominant building models with empirical expressions", Techn. Gazette, 22(3), 685-694.
  24. Hadzima-Nyarko, M., Nyarko, E.K. and Moric, D. (2011), "A neural network based modelling and sensitivity analysis of damage ratio coefficient", Expert Syst. Appl., 38(10), 13405-13413. https://doi.org/10.1016/j.eswa.2011.04.169
  25. Housner, G.W. and Brady, G. (1963), "Natural periods of vibration of buildings", J. Eng. Mech. Div., Proceedings of the American Society of Civil Engineers, 89, No. EM4, 31-65.
  26. Isik, E. and Kutanis, M. (2015), "Performance based assessment for existing residential buildings in Lake Van basin and seismicity of the region", Earthq. Struct., 9(4), 893-910. https://doi.org/10.12989/eas.2015.9.4.893
  27. Israel IC-413 (1994), SI 413 Design provision for Earthquake Resistant of structures, Standard Institute of Israel 1988.
  28. Jalali, A. and Salem, A. (2005), "Fundamental periods of buildings measured from ambien vibration measurements", Proceedings of the 2005 World Sustainable Building Conference, Tokyo.
  29. Joshi, S.G., Shreenivas N. 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
  30. Kalman Sipos, T., Sigmund, V. and Hadzima-Nyarko, M. (2013), "Earthquake performance of infilled frames using neural networks and experimental database", Eng. Struct., 51, 113-127. https://doi.org/10.1016/j.engstruct.2012.12.038
  31. KBC (1988), National Building Code of Korea (KBC), Korean Ministry of Construction, Seoul.
  32. Khademi, F., Akbari, M. and Jamal, S.M.M. (2015), "Prediction of compressive strength of concrete by data-driven models", i-Manager's J. Civil Eng., 5(2), 16.
  33. Kohonen, T. (1989), Self-organization and Associative Memory, 3rd Edition, Springer, New York.
  34. Kose, M.M. (2009), "Parameters affecting the fundamental period of RC buildings with infill walls", Eng. Struct., 31(1), 93-102. https://doi.org/10.1016/j.engstruct.2008.07.017
  35. Kutanis, M., Boru, E.O. and Isik, E. (2016), "Alternative instrumentation schemes for the structural identification of the reinforced concrete field test structure by ambient vibration measurements", KSCE J. Civil Eng., 21(5), 1793-1801
  36. Kwon, O.S. and Kim, E.S. (2010), "Evaluation of building period formulas for seismic design", Earthq. Eng. Struct. Dyn., 39(14), 1569-1583. https://doi.org/10.1002/eqe.998
  37. Lazarevska, M., Knezevic, M., Cvetkovska, M. and Trombeva-Gavriloska, A. (2014), "Application of artificial neural networks in civil engineering", Tech. Gazette, 21(6), 1353-1359.
  38. Lee, L.H., Chang, K.K. and Chun, Y.S. (2000), "Experimental formula for the fundamental period of RC buildings with shearwall dominant system", Struct. Des. Tall Build., 9, 295-307. https://doi.org/10.1002/1099-1794(200009)9:4<295::AID-TAL153>3.0.CO;2-9
  39. Li, Y. and Mau, S.T. (1997), "Learning from recorded earthquake motion of buildings", J. Struct. Eng., 123(1), 62-69. https://doi.org/10.1061/(ASCE)0733-9445(1997)123:1(62)
  40. Massone, L.M. and Ulloa, M.A., (2014), "Shear response estimate for squat reinforced concrete walls via a single panel model", Earthq. Struct., 7(5), 733-751.
  41. Michel, C., Gueguen, P., Lestuzzi, P. and Bard, P.Y. (2010), "Comparison between seismic vulnerability models and experimental dynamic properties of existing buildings in France", Bull. Earthq. Eng., 8, 1295-1307. https://doi.org/10.1007/s10518-010-9185-7
  42. Muhammad, K., Noor Mohammad, N. and Rehman, F. (2015), "Modeling shotcrete mix design using artificial neural network", Comput. Concrete, 15(2), 167-181. https://doi.org/10.12989/cac.2015.15.2.167
  43. NeuroSolutions Getting Started Manual Version 4, Neural Network Based System Identification Toolbox (2005), Retrieved from http://www.neurosolutions.com/.
  44. New Zealand Society of Earthquake Engineering (NZSEE) (2006), Assessment and improvement of the structural performance of buildings in earthquakes, Recommendations of a NZSEE Study Group on Earthquake Risk Buildings.
  45. Nikoo, M., Torabian Moghadam, F. and Sadowski, L. (2015), "Prediction of concrete compressive strength by evolutionary artificial neural networks", Adv. Mater. Sci. Eng., Volume 2015, Article ID 849126, 1-8.
  46. Nikoo, M., Zarfam, P. and Sayahpour, H. (2015), "Determination of compressive strength of concrete using Self Organization Feature Map (SOFM)", Eng. Comput., 31(1), 113-121. https://doi.org/10.1007/s00366-013-0334-x
  47. NSCP (1992), National Structural Code of Philippines, Vol. 1, Fourth Edition, The Board of Civil Engineering of the Professional Regulation Commission, Manila, Philippines.
  48. Oliveira, C.S. and Navarro, M. (2010), "Fundamental periods of vibration of RC buildings in Portugal from in-situ experimental and numerical techniques", Bull. Earthq. Eng., 8(3), 609-642. https://doi.org/10.1007/s10518-009-9162-1
  49. Ozmen, H.B. and Inel, M. (2015), "Damage potential of earthquake records for RC building stock", Earthq. Struct., 10(6), 1315-1330. https://doi.org/10.12989/EAS.2016.10.6.1315
  50. Poovarodom, N., Warnitchai, P., Petcharoen, C., Yinghan, P. and Jantasod, M. (2004), "Dynamic characteristics of nonseismically designed reinforced concrete buildings with soft soil condition in Bangkok", Proceedings of the 13th WCEE, Vancouver, B.C., Canada, Paper No. 1264.
  51. Ricci, P., Verderame, G.M. and Manfredi, G. (2011), "Analytical investigation of elastic period of infilled RC MRF buildings", Eng. Struct., 33, 308-319. https://doi.org/10.1016/j.engstruct.2010.10.009
  52. Sadowski, L. and Hola, J. (2015), "ANN modeling of pull-off adhesion of concrete layers", Adv. Eng. Softw., 89, 17-27. https://doi.org/10.1016/j.advengsoft.2015.06.013
  53. Salama, M.I. (2015), "Estimation of period of vibration for concrete moment-resisting frame buildings", HBRC J., 11, 16-21. https://doi.org/10.1016/j.hbrcj.2014.01.006
  54. SEAOC (1996), Recommended lateral force requirements and commentary, Seismological Engineers Association of California, San Francisco.
  55. Sofi, M., Hutchinson, G.L. and Duffield, C. (2015), "Review of techniques for predicting the fundamental period of multi-storey buildings: effects of nonstructural components", Int. J. Struct. Stab. Dyn., 15(02), 1450039. https://doi.org/10.1142/S0219455414500394
  56. Tay, F.E.H. and Cao, L.J. (2001), "Improved financial time series forecasting by combining support vector machines with selforganizing feature map", Intell. Data Anal., 5(4), 339-354.
  57. UBC-Uniform Building Code (1997), International conference of building officials. Whittier, CA.
  58. Yuan, Z., Wang, L.N. and Ji, X. (2014), "Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS", Adv. Eng. Softw., 67, 156-163. https://doi.org/10.1016/j.advengsoft.2013.09.004
  59. Zhou, Y., Zhou, Y., Yi, W., Chen, T., Tan, D. and Mi, S. (2017), "Operational modal analysis and rational finite-element model selection for ten high-rise buildings based on on-site ambient vibration measurements", J. Perform. Constr. Facil, 31(5), 1-14.

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