Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Prediction of force reduction factor (R) of prefabricated industrial buildings using neural networks

  • Arslan, M. Hakan (Selcuk University) ;
  • Ceylan, Murat (Selcuk University) ;
  • Kaltakci, Yaspr M. (Selcuk University) ;
  • Ozbay, Yuksel (Selcuk University) ;
  • Gulay, Fatma Gulten (Department of Civil Engineering, Istanbul Technical University)
  • Received : 2006.03.20
  • Accepted : 2007.05.04
  • Published : 2007.09.30
⟨ Previous Next ⟩

Abstract

The force (load) reduction factor, R, which is one of the most important parameters in earthquake load calculation, is independent of the dimensions of the structure but is defined on the basis of the load bearing system of the structure as defined in earthquake codes. Significant damages and failures were experienced on prefabricated reinforced concrete structures during the last three major earthquakes in Turkey (Adana 1998, Kocaeli 1999, Duzce 1999) and the experts are still discussing the main reasons of those failures. Most of them agreed that they resulted mainly from the earthquake force reduction factor, R that is incorrectly selected during design processes, in addition to all other detailing errors. Thus this wide spread damages caused by the earthquake to prefabricated structures aroused suspicion about the correctness of the R coefficient recommended in the current Turkish Earthquake Codes (TEC - 98). In this study, an attempt was made for an approximate determination of R coefficient for widely utilized prefabricated structure types (single-floor single-span) with variable dimensions. According to the selecting variable dimensions, 140 sample frames were computed using pushover analysis. The force reduction factor R was calculated by load-displacement curves obtained pushover analysis for each frame. Then, formulated artificial neural network method was trained by using 107 of the 140 sample frames. For the training various algorithms were used. The method was applied and used for the prediction of the R rest 33 frames with about 92% accuracy. The paper also aims at proposing the authorities to change the R coefficient values predicted in TEC - 98 for prefabricated concrete structures.

Keywords

References

  1. Arslan, M.H., Korkmaz, H.H. and Gulay, F.G. (2006), 'Damage and failure pattern of prefabricated structures after major earthquakes in Turkey and shortfalls of the Turkish Earthquake code', Eng. Failure Anal., 13(4), 537-557 https://doi.org/10.1016/j.engfailanal.2005.02.006
  2. Atakoy, H. (2000), The August 17th Earthquake and the Prefabricated Structures Built by the Members of the Prefabric Union. Concrete Prefabrication, 52-53, October 1999-January [in Turkish]
  3. ATC (1978), Tentative Provisions for the Development of Seismic Regulations for Buildings, ATC-3-06, Applied Technology Council, Redwood City, California
  4. ATC-40 (Applied Technology Council) (1996), Seismic Evaluation and Retrofit of Concrete Buildings, Volume 1, ATC, Redwood City, California
  5. El-Nashai, A.M. (2001), 'Advanced inelastic static (pushover) analysis for earthquake applications', Struct. Eng. Mech., 6(2), 239-273
  6. Eurocode 8 (1999), Design Provision for Earthquake Resistance of Structures, Part1-1,1-2 and 1-3, Bruxelles
  7. Federal Emergency Management Agency, FEMA273/256, Guidelines for the Seismic Rehabilitation of Buildings, I, Redwood City, California, USA
  8. Haykin, S. (1994), Neural Networks, A Comprehensive Foundation. New York: Macmillan
  9. http://www.fedpubs.com/subject/housing/natbuilding.htm
  10. http://nisee.berkeley.edu/kobe/codes.html
  11. Iverson, J.K. and Hawkins, N.M. (1994), 'Performance of pre-cast/pre-stressed concrete building structures during the Northridge Earthquake', PCI J
  12. Jewkins, W.M. (1999), 'A neural network for structural re-analysis', Comput. Struct., 687-698
  13. Jang, J.S.R., Sun, C.T. and Mizutani, E. (1997), Neuro-Fuzzy and Soft Computing, Prentice Hall, USA
  14. Kaltakci, M.Y. and Dere, Y. (1997), 'Ultimate strength investigation of light-weight reinforced concrete plates by neural networks', The Supreme Council of Sciences Organizes the 37th Science Week at the Auditoriums of the University of Damascus, 3-18
  15. Kappos, A.J. (2001), 'Evaluation of behaviour factors on the basis of ductility and overstrength studies', Eng. Mech., 12(1), 51-69
  16. Kank, B., Tokhi, M.O. and Alci, M. (2003), 'A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis', IEEE T. Biomedical Eng., 50, 1255-1261 https://doi.org/10.1109/TBME.2003.818469
  17. Kassim, E.AM. and Topping, B.H.V. (1987), 'Static reanalysis', Proc. Am. Soc. Civil Engrs., J. Struct. Eng., 113, 1039-1045
  18. Korkmaz, H.H. and Tankut, T. (2005), 'Analysis of precast concrete joint region', Eng. Struct., 27(9), 1392-1407 https://doi.org/10.1016/j.engstruct.2005.04.004
  19. Maheri and Akbari (2003), 'Seismic behaviour factor, R, for steel X-braced and knee braced RC buildings', Eng. Struct., 1505-1513
  20. Matlab (2006), Neural Networks Toolbox User Guide
  21. Miranda, E. and Bertero, V.V. (1994), 'Evaluation of strength reduction factors for earthquake-resistant design', Earhq. Spectra, 10(2), 357-379 https://doi.org/10.1193/1.1585778
  22. Mwafy, A.S. and Elneshai, A.M. (2002), 'Calibration of force reduction factors of RC buildings', J. Earthq. Eng., 6(2), 239-273 https://doi.org/10.1142/S1363246902000723
  23. Rafiq, Bugmann and Easterbrook, Neural network design for engineering applications', Comput. Struct., (2001) 1541-1552
  24. Standards New Zeland, General Structural Design and Design Loadings for Buildings, NSZ 4203:1992, Wellington
  25. SAP2000 (2000), Structural Analysis Program, Nonlinear Version 7.12, Computer and Structures, Inc. Berkeley, CA, USA
  26. Turkish Earthquake Code (TEC) (1998), Regulations on Structures Constructed in Disaster Regions, Ministry of Public Works and Settlement, Ankara
  27. http:www.seaoc.org/
  28. Tankut, T., Ersoy, U. and Ozcebe, G. (2000), 'Pre-production structural failures in 1999 Marmara and Duzce Earthquakes', In 10th. Prefabrication Symposium, Istanbul, Turkey
  29. TBC-500-2000 (2000), Requirements for Design and Construction of Reinforced Concrete Structures, TSE, Ankara
  30. Tezcan, S.S., Shortfalls of Turkish Earthquake Code, Building Journal, (2005)
  31. August.Tezcan S.S., Shortfalls of Turkish Prefabricated Building Code' IV, Int. Earthquake Symposium, AT-01 (2003) Istanbul, Turkey
  32. Toniolo, G. (2002), The Seismic Design of Pre-cast Concrete Structures in the New Eurocode 8. In: 17th Int. Congress of the Precast Concrete Industry, 1-4 May, Istanbul, Turkey
  33. Uang, C.M. (1991), 'Establishing R and Cd factors for building seismic previsions', J. Struct. Eng., 117(1), 19-28 https://doi.org/10.1061/(ASCE)0733-9445(1991)117:1(19)
  34. Uniform Building Code (1997), International Conference of Building Officials, Whittier, California
  35. Ozbay, Y, Ceylan, R. and Kank, B. (2006), 'A fuzzy clustering neural network architecture for classification of ECG arrhytmias', Comput. Biology Medicine, 36(4), 376-388 https://doi.org/10.1016/j.compbiomed.2005.01.006

Cited by

  1. A new application area of ANN and ANFIS: determination of earthquake load reduction factor of prefabricated industrial buildings vol.27, pp.1, 2010, https://doi.org/10.1080/10286600802506726
  2. Design Force Estimation Using Artificial Neural Network for Groups of Four Cylindrical Silos vol.13, pp.4, 2010, https://doi.org/10.1260/1369-4332.13.4.681
  3. An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks vol.32, pp.7, 2010, https://doi.org/10.1016/j.engstruct.2010.03.010
  4. An investigation on global ductility of strengthened RC frames vol.163, pp.3, 2010, https://doi.org/10.1680/stbu.2010.163.3.177
  5. Concrete compressive strength detection using image processing based new test method vol.109, 2017, https://doi.org/10.1016/j.measurement.2017.05.051
  6. Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network vol.11, pp.6, 2014, https://doi.org/10.1590/S1679-78252014000600004
  7. Shear strength estimation of RC deep beams using the ANN and strut-and-tie approaches vol.57, pp.4, 2016, https://doi.org/10.12989/sem.2016.57.4.657
  8. Investigation of Seismic Behavior and Infill Wall Effects for Prefabricated Industrial Buildings in Turkey vol.25, pp.3, 2011, https://doi.org/10.1061/(ASCE)CF.1943-5509.0000148
  9. Predicting of torsional strength of RC beams by using different artificial neural network algorithms and building codes vol.41, pp.7-8, 2010, https://doi.org/10.1016/j.advengsoft.2010.05.009
  10. Estimation of flexural capacity of quadrilateral FRP-confined RC columns using combined artificial neural network vol.42, 2012, https://doi.org/10.1016/j.engstruct.2012.04.013
  11. Design forces for groups of six cylindrical silos by artificial neural network modelling vol.165, pp.10, 2012, https://doi.org/10.1680/stbu.10.00049
  12. Estimation of curvature and displacement ductility in reinforced concrete buildings vol.16, pp.5, 2012, https://doi.org/10.1007/s12205-012-0958-1
  13. Neural networks for inelastic mid-span deflections in continuous composite beams vol.36, pp.2, 2007, https://doi.org/10.12989/sem.2010.36.2.165
  14. Prediction of moments in composite frames considering cracking and time effects using neural network models vol.39, pp.2, 2007, https://doi.org/10.12989/sem.2011.39.2.267
  15. Evaluation of accidental eccentricity for buildings by artificial neural networks vol.41, pp.4, 2007, https://doi.org/10.12989/sem.2012.41.4.527
  16. Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks vol.21, pp.2, 2007, https://doi.org/10.1515/secm-2013-0002
  17. Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks vol.21, pp.2, 2007, https://doi.org/10.1515/secm-2013-0002
  18. Effect of sequential earthquakes on evaluation of non-linear response of 3D RC MRFs vol.20, pp.3, 2021, https://doi.org/10.12989/eas.2021.20.3.279
  19. Neural networks for the rapid seismic assessment of existing moment-frame RC buildings vol.67, pp.None, 2007, https://doi.org/10.1016/j.ijdrr.2021.102677
  20. State-of-the-art review: Reduction factor of traditional steel moment resisting, braced or eccentrically braced systems vol.35, pp.None, 2007, https://doi.org/10.1016/j.istruc.2021.11.028