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Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques
Joint Authors
Kaloop, Mosbeh R.
Elbeltagi, Emad
Hu, Jong Wan
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-19
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This study investigates predicting the pullout capacity of small ground anchors using nonlinear computing techniques.
The input-output prediction model for the nonlinear Hammerstein-Wiener (NHW) and delay inputs for the adaptive neurofuzzy inference system (DANFIS) are developed and utilized to predict the pullout capacity.
The results of the developed models are compared with previous studies that used artificial neural networks and least square support vector machine techniques for the same case study.
The in situ data collection and statistical performances are used to evaluate the models performance.
Results show that the developed models enhance the precision of predicting the pullout capacity when compared with previous studies.
Also, the DANFIS model performance is proven to be better than other models used to detect the pullout capacity of ground anchors.
American Psychological Association (APA)
Kaloop, Mosbeh R.& Hu, Jong Wan& Elbeltagi, Emad. 2017. Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques. Shock and Vibration،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1204144
Modern Language Association (MLA)
Kaloop, Mosbeh R.…[et al.]. Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques. Shock and Vibration No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1204144
American Medical Association (AMA)
Kaloop, Mosbeh R.& Hu, Jong Wan& Elbeltagi, Emad. Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques. Shock and Vibration. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1204144
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1204144