An innovative approach for modeling of hysteretic energy demand in steel moment resisting frames

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@Article{Gandomi:2014:NCA,
  author =       "Amir Hossein Gandomi and Amir Hossein Alavi and 
                 Abazar Asghari and Hadi Niroomand and Ali Matin Nazar",
  title =        "An innovative approach for modeling of hysteretic
                 energy demand in steel moment resisting frames",
  journal =      "Neural Computing and Applications",
  year =         "2014",
  volume =       "24",
  number =       "6",
  pages =        "1285--1291",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, hysteresis
                 energy, Steel frames, Hybrid genetic simulated
                 annealing, Prediction",
  publisher =    "Springer-Verlag",
  ISSN =         "0941-0643",
  DOI =          "doi:10.1007/s00521-013-1342-x",
  language =     "English",
  size =         "7 pages",
  abstract =     "This paper presents a new nonlinear model for the
                 prediction of Hysteresis energy demand in steel moment
                 resisting frames using an innovative genetic-based
                 simulated annealing method called GSA. The hysteresis
                 energy demand was formulated in terms of several
                 effective parameters such as earthquake intensity,
                 number of stories, soil type, period, strength index,
                 and energy imparted to the structure. The performance
                 and validity of the model were further tested using
                 several criteria. The proposed model provides very high
                 correlation coefficient (R = 0.985), and low root mean
                 absolute error (RMSE = 1,346.1) and mean squared error
                 (MAE = 1,037.6) values. The obtained results indicate
                 that GSA is an effective method for the estimation of
                 the hysteresis energy. The proposed GSA-based model is
                 valuable for routine design practice. The prediction
                 performance of the optimal GSA model was found to be
                 better than that of the existing models.",
}

Genetic Programming entries for A H Gandomi A H Alavi Abazar Asghari Hadi Niroomand Ali Matin Nazar

Citations