Structural Engineering and Mechanics

  • 제90권2호
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  • Pages.189-208
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  • 2024
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

A TSK fuzzy model optimization with meta-heuristic algorithms for seismic response prediction of nonlinear steel moment-resisting frames

  • 투고 : 2023.01.08
  • 심사 : 2024.04.01
  • 발행 : 2024.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

키워드

참고문헌

  1. An, H. and Lee, J.H. (2022), "Deep neural network for prediction of time-history seismic response of bridges", Struct. Eng. Mech., 83(3), 401-413. https://doi.org/10.12989/sem.2022.83.3.401.
  2. Azar, B.F., Veladi, H., Raeesi, F. and Talatahari, S. (2020), "Control of the nonlinear building using an optimum inverse TSK model of MR damper based on modified grey wolf optimizer", Eng. Struct., 214, 110657. https://doi.org/10.1016/j.engstruct.2020.110657.
  3. Azizi, M., Ejlali, R.G., Ghasemi, S.A.M. and Talatahari, S. (2019), "Upgraded Whale Optimization Algorithm for fuzzy logic based vibration control of nonlinear steel structure", Eng. Struct., 192, 53-70. https://doi.org/10.1016/j.engstruct.2019.05.007.
  4. Bai, J., Zhang, J., Jin, S. and Wang, Y. (2021), "A simplified computational model for seismic performance evaluation of steel plate shear wall-frame structural systems", Struct., 33, 1677-1689. https://doi.org/10.1016/j.istruc.2021.05.049.
  5. Berradia, M., Azab, M., Ahmad, Z., Accouche, O., Raza, A. and Alashker, Y. (2022), "Data-driven prediction of compressive strength of FRP-confined concrete members: An application of machine learning models", Struct. Eng. Mech., 83(4), 515-535. https://doi.org/10.12989/sem.2022.83.4.515.
  6. Charrier, M. and Ouellet-Plamondon, C.M. (2022), "Artificial neural network for the prediction of the fresh properties of cementitious materials", Cement Concrete Res., 156, 106761. https://doi.org/10.1016/j.cemconres.2022.106761.
  7. Chatterjee, A. and Watanabe, K. (2006), "An optimized TakagiSugeno type neuro-fuzzy system for modeling robot manipulators", Neur. Comput. Appl., 15(1), 55-61. https://doi.org/10.1007/s00521-005-0008-8.
  8. Chen, M.S. (1999), "A comparative study of learning methods in tuning parameters of fuzzy membership functions", IEEE Trans. Syst. Cybernet., 2, 40-44. https://doi.org/10.1109/icsmc.1999.823150.
  9. Cheng, J., Cai, C.S. and Xiao, R.C. (2007) "Application of artificial neural networks to the response prediction of geometrically nonlinear truss structures", Struct. Eng. Mech., 26(3), 251-262. https://doi.org/10.12989/sem.2007.26.3.251.
  10. Dewan, M.W., Huggett, D.J., Liao, T.W., Wahab, M.A. and Okeil, A.M. (2016), "Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network", Mater. Des., 92, 288-299. https://doi.org/10.1016/j.matdes.2015.12.005.
  11. Feng, G. (2010), Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach, Taylor & Francis Group, Boca Raton.
  12. Hajirasouliha, I. and Doostan, A. (2010), "A simplified model for seismic response prediction of concentrically braced frames", Adv. Eng. Softw., 41(3), 497-505. https://doi.org/10.1016/j.advengsoft.2009.10.008.
  13. 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.
  14. Hamidian, D., Salajegheh, E. and Salajegheh, J. (2018), "Damage detection technique for irregular continuum structures using wavelet transform and fuzzy inference system optimized by particle swarm optimization", Struct. Eng. Mech., 67(5), 457-464. https://doi.org/10.12989/sem.2018.67.5.457.
  15. Harooni, A.B. and Marghmaleki, A.N. (2017), "Implementing a PSO-ANFIS model for prediction of viscosity of mixed oils", Petrol. Sci. Technol., 35(2), 155-162. https://doi.org/10.1080/10916466.2016.1256899.
  16. Holland, J.H. (1992), "Genetic algorithms", Scientif. Am., 267(1), 66-73. https://doi.org/10.1038/scientificamerican0792-66
  17. Jang, J.S.R. (1993), "ANFIS: Adaptive-network-based fuzzy inference system", IEEE Trans. Syst., Man Cybernet., 23(3), 665-685. https://doi.org/10.1109/21.256541.
  18. Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of ICNN'95-International Conference on Neural Networks, 4, 1942-1948. https://doi.org/10.1109/ICNN.1995.488968.
  19. Kukolj, D. (2002), "Design of adaptive Takagi-Sugeno-Kang fuzzy models", Appl. Soft Comput., 2, 89-103. https://doi.org/10.1016/S1568-4946(02)00032-7.
  20. Lai, X., He, Z. and Wu, Y. (2021), "Elastic inter-story drift seismic demand estimate of super high-rise buildings using coupled flexural-shear model with mass and stiffness non-uniformities", Eng. Struct., 226, 111378. https://doi.org/10.1016/j.engstruct.2020.111378.
  21. Lee, S., Vo, T.P., Thai, H.T., Lee, J. and Patel, V. (2021), "Strength prediction of concrete-filled steel tubular columns using Categorical Gradient Boosting algorithm", Eng. Struct., 238, 112109. https://doi.org/10.1016/j.engstruct.2021.112109.
  22. Mamdani, E.H. (1974), "Applications of fuzzy algorithms for simple dynamic plants", Proced. IEEE, 121(12), 1585-1588.
  23. Miranda, B. (1999), "Approximate seismic lateral deformation demands in multistory buildings", J. Struct. Eng., 125, 417-425. https://doi.org/10.1061/(ASCE)0733-9445(1999)125:4(417).
  24. Mirjalili, S., Mirjalili, S.M. and Hatamlou, A. (2016), "Multi-verse optimizer: A nature-inspired algorithm for global optimization", Neur. Comput. Appl., 27, 495-513. https://doi.org/10.1007/s00521-015-1870-7.
  25. Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, M., Jameel, M., Hakim, S.J.S. and Zargar, M. (2013), "Application of the ANFIS model in deflection prediction of concrete deep beam", Struct. Eng. Mech., 45(3), 319-332. https://doi.org/10.12989/sem.2013.45.3.323.
  26. Newmark, N.M. (1959), "A method of computation for structural dynamics", J. Eng. Mech. Div., 85(3), 67-94. https://doi.org/10.1061/JMCEA3.0000098.
  27. Nguyen, H.D., Dao, N.D. and Shin, M. (2021) "Prediction of seismic drift responses of planar steel moment frames using artificial neural network and extreme gradient boosting", Eng. Struct., 242, 112518. https://doi.org/10.1016/j.engstruct.2021.112518.
  28. Ohtori, Y., Christenson, R.E., Spencer, B.F. amd Dyke, S.J. (2004), "Benchmark control problems for seismically excited nonlinear buildings", J. Eng. Mech., 130(4), 366-385. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:4(366).
  29. Prasanna, P.K., Murthy, A.R. and Srinivasu, K. (2018), "Prediction of compressive strength of GGBS based concrete using RVM", Struct. Eng. Mech., 68(6), 691-700. https://doi.org/10.12989/sem.2018.68.6.691.
  30. Samimifar, M., Massumi, A. and Moghadam, A.S. (2019), "A new practical equivalent linear model for estimating seismic hysteretic energy demand of bilinear systems", Struct. Eng. Mech., 70(3), 289-301. https://doi.org/10.12989/sem.2019.70.3.289.
  31. Shirgir, S., Azar, B.F. and Hadidi, A. (2020), "Opposition based charged system search for parameter identification problem in a simplified Bouc-Wen model", Earthq. Struct., 18(4), 493-506. https://doi.org/10.12989/eas.2020.18.4.493.
  32. Takagi, T. and Sugeno, M. (1985), "Fuzzy identification of systems and its applications to modeling and control", IEEE Trans. Syst., Man Cybernet., 15(1), 116-132. https://doi.org/10.1109/TSMC.1985.6313399.
  33. Thaler, D., Stoffel, M., Markert, B. and Bamer, F. (2021), "Machine-learning-enhanced tail end prediction of structural response statistics in earthquake engineering", Earthq. Eng. Struct. Dyn., 50(8), 2098-2114. https://doi.org/10.1002/eqe.3432.
  34. Thirumalaiselvi, A., Verma, M., Anandavalli, N. and Rajasankar, J. (2018), "Response prediction of laced steel-concrete composite beams using machine learning algorithms", Struct. Eng. Mech., 66(3), 399-409. https://doi.org/10.12989/sem.2018.66.3.399.
  35. Tijani, I.A., Lawal, A.I. and Kwon, S. (2022), "Machine learning techniques for prediction of ultimate strain of FRP-confined concrete", Struct. Eng. Mech., 84(1), 101-111. https://doi.org/10.12989/sem.2022.84.1.101.
  36. Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Comput. Mater. Sci., 41, 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009.
  37. Woo, Z., Hoon, J. and Loganathan, G.V. (2001), "A New Heuristic Optimization Algorithm: Harmony Search", Simulation, 76(2), 60-68. https://doi.org/10.1177/003754970107600201.
  38. Yinfeng, D., Yingmin, L., Ming, L. and Mingkui, X. (2008), "Nonlinear structural response prediction based on support vector machines", J. Sound Vib., 311, 886-897. https://doi.org/10.1016/j.jsv.2007.09.054.
  39. Zadeh, L.A. (1965), "Fuzzy sets", Inform. Control, 8, 338-353. https://doi.org/10.1142/9789814261302_0021.