An innovative approach for modeling of hysteretic energy demand in steel moment resisting frames
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @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