Structural Engineering and Mechanics

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An improvement on fuzzy seismic fragility analysis using gene expression programming

  • Ebrahimi, Elaheh (Faculty of Civil Engineering, Babol Noshirvani University of Technology) ;
  • Abdollahzadeh, Gholamreza (Faculty of Civil Engineering, Babol Noshirvani University of Technology) ;
  • Jahani, Ehsan (Department of Civil Engineering, University of Mazandaran)
  • Received : 2021.06.15
  • Accepted : 2022.03.15
  • Published : 2022.09.10
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Abstract

This paper develops a comparatively time-efficient methodology for performing seismic fragility analysis of the reinforced concrete (RC) buildings in the presence of uncertainty sources. It aims to appraise the effectiveness of any variation in the material's mechanical properties as epistemic uncertainty, and the record-to-record variation as aleatory uncertainty in structural response. In this respect, the fuzzy set theory, a well-known 𝛼-cut approach, and the Genetic Algorithm (GA) assess the median of collapse fragility curves as a fuzzy response. GA is requisite for searching the maxima and minima of the objective function (median fragility herein) in each membership degree, 𝛼. As this is a complicated and time-consuming process, the authors propose utilizing the Gene Expression Programming-based (GEP-based) equation for reducing the computational analysis time of the case study building significantly. The results indicate that the proposed structural analysis algorithm on the derived GEP model is able to compute the fuzzy median fragility about 33.3% faster, with errors less than 1%.

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References

  1. Abdollahzadeh, G., Jahani, E. and Kashir, Z. (2016), "Predicting of compressive strength of recycled aggregate concrete by genetic programming", Comput. Concrete, 18(2), 155-163. http://doi.org/10.12989/cac.2016.18.2.155.
  2. Abdollahzadeh, G., Jahani, E. and Kashir, Z. (2017), "Genetic programming based formulation to predict compressive strength of high strength concrete", Civil Eng. Infrastr. J., 50(2), 207-219. http://doi.org/10.7508/ceij.2017.02.001.
  3. Adeli, H. (2001), "Neural networks in civil engineering: 1989-2000", Comput.-Aid. Civil Infrastr. Eng., 16(2), 126-142. https://doi.org/10.1111/0885-9507.00219.
  4. Alavi, A.H., Ameri, M., Gandomi, A.H. and Mirzahosseini, M.R. (2011), "Formulation of flow number of asphalt mixes using a hybrid computational method", Constr. Build. Mater., 25, 1338-1355. https://doi.org/10.1016/j.conbuildmat.2010.09.010.
  5. Ang, A.H.S. and Tang, W. (1975), Probability Concepts in Engineering Planning and Design, Vol. 1: Basic Principles, John Wiley, New York, NY, USA.
  6. Civanlar, M.R. and Trussell, H.J. (1986), "Constructing membership functions using statistical data", Fuzzy Set. Syst., 18(1), 1-13. https://doi.org/10.1016/0165-0114(86)90024-2.
  7. Ebrahimi, E., Abdollahzadeh, G. and Jahani, E. (2020), "Developing the seismic fragility analysis with fuzzy random variables using Mouth Brooding Fish algorithm", Appl. Soft Comput., 91, 106190. https://doi.org/10.1016/j.asoc.2020.106190.
  8. FEMA-P695 (2009), Quantification of Building Seismic Performance Factors, Federal Emergency Management Agency, Washington, D.C., USA.
  9. Ferreira, C. (2001), "Gene expression programming: A new adaptive algorithm for solving problems", Complex Syst., 13(2), 87-129. https://doi.org/10.48550/arXiv.cs/0102027.
  10. Ferreira, C. (2006), Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, Vol. 21, Springer, Berlin/Heidelberg, Germany.
  11. Garakaninezhad, A. and Bastami, M. (2019), "An evolutionary approach for structural reliability", Struct. Eng. Mech., 71(4), 329-339. https://doi.org/10.12989/sem.2019.71.4.329.
  12. Ghiamat, R., Madhkhan, M. and Bakhshpoori, T. (2019), "Cost optimization of segmental precast concrete bridges superstructure using genetic algorithm", Struct. Eng. Mech., 72(4), 503-512. https://doi.org/10.12989/sem.2019.72.4.503.
  13. Gholampour, A., Gandomi, A.H. and Ozbakkaloglu, T. (2017), "New formulations for mechanical properties of recycled aggregate concrete using gene expression programming", Constr. Build. Mater., 130, 122-145. https://doi.org/10.1016/j.conbuildmat.2016.10.114.
  14. Golberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Boston, MA, USA.
  15. Goldberg, D.E. (1989), Genetic Algorithm in Search, Optimization, and Machine Learning, Reading, Addison-Wesley, MA.
  16. Guan, J.W. and Bell, D.A. (1991), Evidence Theory and Its Applications, Vols. 1 and 2, North-Holland, New York, NY, USA.
  17. Hanss, M. and Turrin, S. (2010), "A fuzzy-based approach to comprehensive modeling and analysis of systems with epistemic uncertainties", Struct. Saf., 32(6), 433-441. https://doi.org/10.1016/j.strusafe.2010.06.003.
  18. Haselton, C.B. and Deierlein, G.G. (2007), "Assessing seismic collapse safety of modern reinforced concrete moment framing buildings", Research Report No. 156, The John A. Blume earthquake Engineering Center, Department of Civil and Environmental Engineering, Stanford University, CA, USA.
  19. Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, USA.
  20. Ibarra, L.F. and Krawinkler, H. (2005), "Global collapse of frame structures under seismic excitations", Pacific Earthquake Engineering Research Center, Berkeley, CA, USA.
  21. Ibarra, L.F., Medina, R.A. and Krawinkler, H. (2005), "Hysteretic models that incorporate strength and stiffness deterioration", Earthq. Eng. Struct. Dyn., 34, 1489-1511. https://doi.org/10.1002/eqe.495.
  22. Jahani, E., Muhanna, R.L., Shayanfar, M.A, and Barkhordari, M.A. (2014), "Reliability assessment with fuzzy random variables using interval Monte Carlo simulation", Comput.-Aid. Civil Infrastr. Eng., 29(3), 208-220. https://doi.org/10.1111/mice.12028.
  23. Jahani, E., Shayanfar, M.A, and Barkhordari, M.A. (2013), "A new adaptive importance sampling Monte Carlo method for structural reliability", KSCE J. Civil Eng., 17(1), 210-215. https://doi.org/10.1007/s12205-013-1779-6.
  24. Jahani, E., Shayanfar, M.A. Barkhordari, M.A. (2013), "Structural reliability based on Genetic Algorithm-Monte Carlo (GAMC)", Adv. Struct. Eng., 16(2), 419-426. https://doi.org/10.1260/1369-4332.16.2.419.
  25. Jenkins, W.M. (1991), "Towards structural optimization via the genetic algorithm", Comput. Struct., 40(5), 1321-1327. https://doi.org/10.1016/0045-7949(91)90402-8.
  26. Kiureghian, A.D. and Ditlevsen, O. (2009), "Aleatory or epistemic? Does it matter?", Struct. Saf., 31, 105-112. https://doi.org/10.1016/j.strusafe.2008.06.020.
  27. Kostinakis, K.G. and Morfidis, K.E. (2020), "Optimization of the seismic performance of masonry infilled R/C buildings at the stage of design using artificial neural networks", Struct. Eng. Mech., 75(3), 295-309. https://doi.org/10.12989/sem.2020.75.3.295.
  28. Koza, J.R. (1992), Genetic Programming, MIT Press, Cambridge, MA, USA.
  29. Miano, A., de Silva, D., Compagnone, A. and Chiumiento, G. (2020), "Probabilistic seismic and fire assessment of an existing reinforced concrete building and retrofit design", Struct. Eng. Mech., 74(4), 481-494. https://doi.org/10.12989/sem.2020.74.4.481.
  30. Moens, D. and Vandepitte, D. (2005), "A fuzzy finite element procedure for the calculation of uncertain frequency-response functions of damped structures: Part 1-Procedure", J. Sound Vib., 288(3), 431-462. https://doi.org/10.1016/j.jsv.2005.07.002.
  31. Moller, B., Beer, M., Graf, W. and Hoffmann, A. (1999), "Possibility theory based safety assessment", Comput.-Aid. Civil Infrastr. Eng., 14, 81-91. https://doi.org/10.1111/0885-9507.00132.
  32. Moller, B., Graf, W. and Beer, M. (2000), "Fuzzy structural analysis using α-Level optimization", Comput. Mech., 26(6), 547-565. https://doi.org/10.1007/s004660000204.
  33. Mousavi, S.M., Alavi, A.H., Gandomi, A.H., Arab Esmaeili, M. and Gandomi, M. (2010), "A data mining approach to compressive strength of CFRP-confined concrete cylinders", Struct. Eng. Mech., 36(6), 759. https://doi.org/10.12989/sem.2010.36.6.759.
  34. OpenSees (2021), Open System for Earthquake Engineering Simulation; Pacific Earthquake Engineering Research Center, Berkeley, USA. http://opensees.berkeley.edu.
  35. Shafigh, A., Ahmadi, H.R. and Bayat, M. (2021), "Seismic investigation of cyclic pushover method for regular reinforced concrete bridge", Struct. Eng. Mech., 78(1), 41-52. https://doi.org/10.12989/sem.2021.78.1.041.
  36. Shome, N. and Cornell, C.A. (1999), Probabilistic Seismic Demand Analysis of Nonlinear Structures, Stanford University, Stanford, CA, USA.
  37. Smith, G.N. (1986), Probability and Statistics in Civil Engineering, Collins Professional and Technical Books.
  38. Tao, Y.R., Wang, Q., Cao, L., Duan, S.Y., Huang, Z.H.H. and Cheng, G.Q. (2017), "A novel evidence theory model and combination rule for reliability estimation of structures", Struct. Eng. Mech., 62(4), 507-517. https://doi.org/10.12989/sem.2017.62.4.507.
  39. Valliappan, S. and Pham, T.D. (1993), "Fuzzy finite element analysis of a foundation on an elastic soil medium", Int. J. Numer. Anal. Meth. Geomech., 17(11), 771-789. https://doi.org/10.1002/nag.1610171103.
  40. Vamvatsikos, D. and Cornell, C.A. (2002), "Incremental dynamic analysis", Earthq. Eng. Struct. Dyn., 31(3), 491-514. https://doi.org/10.1002/eqe.141.
  41. Zadeh, L.A. (1978), "Fuzzy sets as a basis for a theory of possibility", Fuzzy Set. Syst., 1(1), 3-28. https://doi.org/10.1016/0165-0114(78)90029-5.
  42. Zareian, F. and Krawinkler, H. (2009), "Simplified performancebased earthquake engineering", Research Report No. 169, The John A. Blume Earthquake Engineering Center, Stanford University, Stanford, CA, USA.
  43. Zareian, F. and Medina, R.A. (2010), "A practical method for proper modeling of structural damping in inelastic plane structural systems", Comput. Struct., 88(1-2), 45-53. https://doi.org/10.1016/j.compstruc.2009.08.001.
  44. Zimmermann, H.J. (1992), Fuzzy Set Theory and Its Applications, Kluwer Academic Publishers, Boston/London.