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Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches
Joint Authors
Source
Advances in Materials Science and Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-02
Country of Publication
Egypt
No. of Pages
12
Abstract EN
Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design and management of structures, especially for the high-risk areas.
In this study, novel applications of advanced soft computing techniques are utilized for predicting the behavior of centrically braced frame (CBF) buildings with lead-rubber bearing (LRB) isolation system under ground motion effects.
These techniques include least square support vector machine (LSSVM), wavelet neural networks (WNN), and adaptive neurofuzzy inference system (ANFIS) along with wavelet denoising.
The simulation of a 2D frame model and eight ground motions are considered in this study to evaluate the prediction models.
The comparison results indicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.
American Psychological Association (APA)
Kaloop, Mosbeh R.& Hu, Jong Wan. 2017. Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches. Advances in Materials Science and Engineering،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1124779
Modern Language Association (MLA)
Kaloop, Mosbeh R.& Hu, Jong Wan. Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches. Advances in Materials Science and Engineering No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1124779
American Medical Association (AMA)
Kaloop, Mosbeh R.& Hu, Jong Wan. Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches. Advances in Materials Science and Engineering. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1124779
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1124779