Prediction of punching shear capacity of RC flat slabs using artificial neural network
Punching shear of flat slabs is a local, brittle failure that may occur before the more favourable ductile flexural failure. This study develops an artificial neural network (ANN) modelling for the prediction of punching shear strength of flat slabs using 281 test data available in the literature. T...
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Springer Cham
2017
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author | Safiee, Nor Azizi Ashour, Ashraf |
author_facet | Safiee, Nor Azizi Ashour, Ashraf |
author_sort | Safiee, Nor Azizi |
collection | UPM |
description | Punching shear of flat slabs is a local, brittle failure that may occur before the more favourable ductile flexural failure. This study develops an artificial neural network (ANN) modelling for the prediction of punching shear strength of flat slabs using 281 test data available in the literature. The paper also evaluates the current design codes for the prediction of punching shear capacity of reinforced concrete flat slabs using the test results reported in the literature. Furthermore, a parametric study was conducted using the trained ANN to establish the trend of the main influencing variables on the punching shear capacity of flat slabs. The results were, then, employed to develop a simplified equation for the prediction of the characteristic/design punching shear strength of flat slabs based on the design assisted by testing approach proposed in Annex D of EN 1990. |
first_indexed | 2024-03-06T09:43:14Z |
format | Article |
id | upm.eprints-62800 |
institution | Universiti Putra Malaysia |
last_indexed | 2024-03-06T09:43:14Z |
publishDate | 2017 |
publisher | Springer Cham |
record_format | dspace |
spelling | upm.eprints-628002022-12-01T06:35:47Z http://psasir.upm.edu.my/id/eprint/62800/ Prediction of punching shear capacity of RC flat slabs using artificial neural network Safiee, Nor Azizi Ashour, Ashraf Punching shear of flat slabs is a local, brittle failure that may occur before the more favourable ductile flexural failure. This study develops an artificial neural network (ANN) modelling for the prediction of punching shear strength of flat slabs using 281 test data available in the literature. The paper also evaluates the current design codes for the prediction of punching shear capacity of reinforced concrete flat slabs using the test results reported in the literature. Furthermore, a parametric study was conducted using the trained ANN to establish the trend of the main influencing variables on the punching shear capacity of flat slabs. The results were, then, employed to develop a simplified equation for the prediction of the characteristic/design punching shear strength of flat slabs based on the design assisted by testing approach proposed in Annex D of EN 1990. Springer Cham 2017-01 Article PeerReviewed Safiee, Nor Azizi and Ashour, Ashraf (2017) Prediction of punching shear capacity of RC flat slabs using artificial neural network. Asian Journal of Civil Engineering, 18 (2). 285 - 309. ISSN 1563-0854; ESSN: 2522-011X https://www.researchgate.net/publication/316634574_Prediction_of_punching_shear_capacity_of_RC_flat_slabs_using_artificial_neural_network |
spellingShingle | Safiee, Nor Azizi Ashour, Ashraf Prediction of punching shear capacity of RC flat slabs using artificial neural network |
title | Prediction of punching shear capacity of RC flat slabs using artificial neural network |
title_full | Prediction of punching shear capacity of RC flat slabs using artificial neural network |
title_fullStr | Prediction of punching shear capacity of RC flat slabs using artificial neural network |
title_full_unstemmed | Prediction of punching shear capacity of RC flat slabs using artificial neural network |
title_short | Prediction of punching shear capacity of RC flat slabs using artificial neural network |
title_sort | prediction of punching shear capacity of rc flat slabs using artificial neural network |
work_keys_str_mv | AT safieenorazizi predictionofpunchingshearcapacityofrcflatslabsusingartificialneuralnetwork AT ashourashraf predictionofpunchingshearcapacityofrcflatslabsusingartificialneuralnetwork |