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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2014

01.05.2014 | Original Article

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

verfasst von: Amir Hossein Gandomi, Amir Hossein Alavi, Abazar Asghari, Hadi Niroomand, Ali Matin Nazar

Erschienen in: Neural Computing and Applications | Ausgabe 6/2014

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Abstract

This paper presents a new nonlinear model for the prediction of hysteretic energy demand in steel moment resisting frames using an innovative genetic-based simulated annealing method called GSA. The hysteretic 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 hysteretic 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.
Vorheriger Artikel Robust t-distribution mixture modeling via spatially directional information
Nächster Artikel Application of SVR, Taguchi loss function, and the artificial bee colony algorithm to resolve multiresponse parameter design problems: a case study on optimizing the design of a TIR lens

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Metadaten
Titel
An innovative approach for modeling of hysteretic energy demand in steel moment resisting frames
verfasst von
Amir Hossein Gandomi
Amir Hossein Alavi
Abazar Asghari
Hadi Niroomand
Ali Matin Nazar
Publikationsdatum
01.05.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1342-x

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