A hybrid computational intelligence approach to predict spectral acceleration

Akhani, M; Kashani, AR; Mousavi, M; Gandomi, AH

A hybrid computational intelligence approach to predict spectral acceleration

Akhani, M Kashani, AR Mousavi, M Gandomi, AH

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Publication Type:: Journal Article
Citation:: Measurement: Journal of the International Measurement Confederation, 2019, 138 pp. 578 - 589
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Akhani, M	en_US
dc.contributor.author	Kashani, AR	en_US
dc.contributor.author	Mousavi, M	en_US
dc.contributor.author	Gandomi, AH https://orcid.org/0000-0002-2798-0104	en_US
dc.date.issued	2019-05-01	en_US
dc.identifier.citation	Measurement: Journal of the International Measurement Confederation, 2019, 138 pp. 578 - 589	en_US
dc.identifier.issn	0263-2241	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136872
dc.description.abstract	© 2019 Elsevier Ltd In this study, a new explicit method has been suggested to predict the spectral acceleration characteristic of strong ground-motions based on hybridizing genetic algorithm (GA), multilayer perceptron neural network (MLPNN), and regression analysis (RA), called GA-NN-RA. The predictor variables encompass a period of vibration, magnitude, closest distance co-seismic rupture, shear wave velocity averaged over the top 30 m and flag for reverse faulting earthquakes. To develop the model, a data set of strong ground-motion records gathered by Pacific Earthquake Engineering Research Center has been employed. For confirmation and efficiency of proposed model, an additional set of test that is not involved in the modeling has been applied. The obtained results using GA-NN-RA show good accuracy in comparison with other ground motion models. Also, the proposed model is capable of evaluating the spectral acceleration for any records without restriction in a period of vibration.	en_US
dc.relation.ispartof	Measurement: Journal of the International Measurement Confederation	en_US
dc.relation.isbasedon	10.1016/j.measurement.2019.02.054	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	A hybrid computational intelligence approach to predict spectral acceleration	en_US
dc.type	Journal Article
utslib.citation.volume	138	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	138	en_US

Abstract:

© 2019 Elsevier Ltd In this study, a new explicit method has been suggested to predict the spectral acceleration characteristic of strong ground-motions based on hybridizing genetic algorithm (GA), multilayer perceptron neural network (MLPNN), and regression analysis (RA), called GA-NN-RA. The predictor variables encompass a period of vibration, magnitude, closest distance co-seismic rupture, shear wave velocity averaged over the top 30 m and flag for reverse faulting earthquakes. To develop the model, a data set of strong ground-motion records gathered by Pacific Earthquake Engineering Research Center has been employed. For confirmation and efficiency of proposed model, an additional set of test that is not involved in the modeling has been applied. The obtained results using GA-NN-RA show good accuracy in comparison with other ground motion models. Also, the proposed model is capable of evaluating the spectral acceleration for any records without restriction in a period of vibration.

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http://hdl.handle.net/10453/136872