Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network
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...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/4039407 |
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| author | E. Khalilzadeh Vahidi M. M. Roshani |
| author_facet | E. Khalilzadeh Vahidi M. M. Roshani |
| author_sort | E. Khalilzadeh Vahidi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-558410c3a1bd4dbe94c6fa4bc4e739dc |
| institution | Kabale University |
| issn | 2314-4904 2314-4912 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-558410c3a1bd4dbe94c6fa4bc4e739dc2025-02-03T06:42:12ZengWileyJournal of Engineering2314-49042314-49122016-01-01201610.1155/2016/40394074039407Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural NetworkE. Khalilzadeh Vahidi0M. M. Roshani1Civil Engineering Department, Razi University, Kermanshah 671497346, IranYoung Researchers and Elite Club, Islamic Azad University, Kermanshah Branch, Kermanshah 6718997551, IranThe 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.http://dx.doi.org/10.1155/2016/4039407 |
| spellingShingle | E. Khalilzadeh Vahidi M. M. Roshani Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network Journal of Engineering |
| title | Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network |
| title_full | Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network |
| title_fullStr | Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network |
| title_full_unstemmed | Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network |
| title_short | Prediction of Load-Carrying Capacity in Steel Shear Wall with Opening Using Artificial Neural Network |
| title_sort | prediction of load carrying capacity in steel shear wall with opening using artificial neural network |
| url | http://dx.doi.org/10.1155/2016/4039407 |
| work_keys_str_mv | AT ekhalilzadehvahidi predictionofloadcarryingcapacityinsteelshearwallwithopeningusingartificialneuralnetwork AT mmroshani predictionofloadcarryingcapacityinsteelshearwallwithopeningusingartificialneuralnetwork |