Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network
Joint Authors
Roshani, Mohammad Mahdi
Khalilzadeh Vahidi, E.
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-05-03
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The effects of different parameters on steel plate shear wall (SPSW) are investigated.
The studied parameters are thickness of plate, location of the opening, thickness of diagonal stiffeners, and thickness of circular stiffener.
Load-carrying capacity of the SPSW is studied under static load using nonlinear geometrical and material analysis in ABAQUS and the obtained simulation results are verified.
An artificial neural network (ANN) is proposed to model the effects of these parameters.
According to the results the circular stiffener has more effect compared with the diagonal stiffeners.
However, the thickness of the plate has the most significant effect on the SPSW behavior.
The results show that the best place for the opening location is the center of SPSW.
Multilayer perceptron (MLP) neural network was used to predict the maximum load in SPSW with opening.
The predicted maximum load values using the proposed MLP model were compared with the simulated validated data.
The obtained results show that the proposed ANN model has achieved good agreement with the validated simulated data, with correlation coefficient of more than 0.9975.
Therefore, the proposed model is useful, reliable, fast, and cheap tools to predict the maximum load in SPSW.
American Psychological Association (APA)
Khalilzadeh Vahidi, E.& Roshani, Mohammad Mahdi. 2016. Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network. Journal of Engineering،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1108366
Modern Language Association (MLA)
Khalilzadeh Vahidi, E.& Roshani, Mohammad Mahdi. Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network. Journal of Engineering No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1108366
American Medical Association (AMA)
Khalilzadeh Vahidi, E.& Roshani, Mohammad Mahdi. Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network. Journal of Engineering. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1108366
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1108366