A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Shear strength prediction of steel fiber reinforced concrete beams without transverse reinforcements
https://orcid.org/http://orcid.org/0000-0001-5944-2071
The objective of this study is to predict the shear strength (SS) of SFRC beams using the artificial neural network (ANN) and nonlinear regression analysis (NRA) based on a comprehensive database consisting of the test results of 398 SFRC beams from past experimental investigations that experienced shear failure. The ANN model is related to the effect of the concrete strength, the longitudinal reinforcement ratio, the shear span–depth ratio, the fiber factor, and the size effect. The developed ANN model correlated well with the available test results and was employed to conduct a parametric study to examine the influence of the main parameters on the SS. In addition, a simple design model is derived based on NRA that consistently predicts the SS.
Shear strength prediction of steel fiber reinforced concrete beams without transverse reinforcements
https://orcid.org/http://orcid.org/0000-0001-5944-2071
The objective of this study is to predict the shear strength (SS) of SFRC beams using the artificial neural network (ANN) and nonlinear regression analysis (NRA) based on a comprehensive database consisting of the test results of 398 SFRC beams from past experimental investigations that experienced shear failure. The ANN model is related to the effect of the concrete strength, the longitudinal reinforcement ratio, the shear span–depth ratio, the fiber factor, and the size effect. The developed ANN model correlated well with the available test results and was employed to conduct a parametric study to examine the influence of the main parameters on the SS. In addition, a simple design model is derived based on NRA that consistently predicts the SS.
Shear strength prediction of steel fiber reinforced concrete beams without transverse reinforcements
https://orcid.org/http://orcid.org/0000-0001-5944-2071
The objective of this study is to predict the shear strength (SS) of SFRC beams using the artificial neural network (ANN) and nonlinear regression analysis (NRA) based on a comprehensive database consisting of the test results of 398 SFRC beams from past experimental investigations that experienced shear failure. The ANN model is related to the effect of the concrete strength, the longitudinal reinforcement ratio, the shear span–depth ratio, the fiber factor, and the size effect. The developed ANN model correlated well with the available test results and was employed to conduct a parametric study to examine the influence of the main parameters on the SS. In addition, a simple design model is derived based on NRA that consistently predicts the SS.
Shear strength prediction of steel fiber reinforced concrete beams without transverse reinforcements
Asian J Civ Eng
Al-Bayati, Ahmed Faleh (author) / Taki, Zahir Noori M. (author)
Asian Journal of Civil Engineering ; 25 ; 1857-1875
2024-02-01
19 pages
Article (Journal)
Electronic Resource
English
Shear strength prediction of steel fiber reinforced concrete beams without transverse reinforcements
| Springer Verlag | 2024
Shear behavior model for steel fiber-reinforced concrete members without transverse reinforcements
| British Library Online Contents | 2012
Shear strength of reinforced recycled aggregate concrete beams without shear reinforcements
| BASE | 2017
Shear strength of reinforced recycled aggregate concrete beams without shear reinforcements
| DOAJ | 2017
Shear strength of reinforced recycled aggregate concrete beams without shear reinforcements
| BASE | 2017