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