A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders
In this paper, the feed-forward backpropagation neural network (FFBPNN) is used to propose a new formulation for predicting the compressive strength of fiber-reinforced polymer (FRP)-confined concrete cylinders. A set of experimental data has been considered in the analysis. The data include informa...
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doaj-f54fc134e2f94df99a025b5236c1daab2020-11-25T00:42:31ZengMDPI AGApplied Sciences2076-34172020-03-01105176910.3390/app10051769app10051769A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete CylindersReza Kamgar0Hosein Naderpour1Houman Ebrahimpour Komeleh2Anna Jakubczyk-Gałczyńska3Robert Jankowski4Department of Civil Engineering, Shahrekord University, Shahrekord 8818634141, IranFaculty of Civil Engineering, Semnan University, Semnan 98 23, IranDepartment of Civil Engineering, Shahid Bahonar University of Kerman, Kerman 7616913439, IranFaculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, PolandFaculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, PolandIn this paper, the feed-forward backpropagation neural network (FFBPNN) is used to propose a new formulation for predicting the compressive strength of fiber-reinforced polymer (FRP)-confined concrete cylinders. A set of experimental data has been considered in the analysis. The data include information about the dimensions of the concrete cylinders (diameter, length) and the total thickness of FRP layers, unconfined ultimate concrete strength, ultimate confinement pressure, ultimate tensile strength of the FRP laminates and the ultimate concrete strength of the concrete cylinders. The confined ultimate concrete strength is considered as the output data, while other parameters are considered as the input data. These parameters are mostly used in existing FRP-confined concrete models. Soft computing techniques are used to estimate the compressive strength of FRP-confined concrete cylinders. Finally, a new formulation is proposed. The results of the proposed formula are compared to the existing methods. To verify the proposed method, results are compared with other methods. The results show that the described method can forecast the compressive strength of FRP-confined concrete cylinders with high precision in comparison with the existing formulas. Moreover, the mean percentage of error for the proposed method is very low (3.49%). Furthermore, the proposed formula can estimate the ultimate compressive capacity of FRP-confined concrete cylinders with a different type of FRP and arbitrary thickness in the initial design of practical projects.https://www.mdpi.com/2076-3417/10/5/1769frpsoft computingcompressive strengthconfined concreteartificial neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Reza Kamgar Hosein Naderpour Houman Ebrahimpour Komeleh Anna Jakubczyk-Gałczyńska Robert Jankowski |
spellingShingle |
Reza Kamgar Hosein Naderpour Houman Ebrahimpour Komeleh Anna Jakubczyk-Gałczyńska Robert Jankowski A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders Applied Sciences frp soft computing compressive strength confined concrete artificial neural network |
author_facet |
Reza Kamgar Hosein Naderpour Houman Ebrahimpour Komeleh Anna Jakubczyk-Gałczyńska Robert Jankowski |
author_sort |
Reza Kamgar |
title |
A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders |
title_short |
A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders |
title_full |
A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders |
title_fullStr |
A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders |
title_full_unstemmed |
A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders |
title_sort |
proposed soft computing model for ultimate strength estimation of frp-confined concrete cylinders |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-03-01 |
description |
In this paper, the feed-forward backpropagation neural network (FFBPNN) is used to propose a new formulation for predicting the compressive strength of fiber-reinforced polymer (FRP)-confined concrete cylinders. A set of experimental data has been considered in the analysis. The data include information about the dimensions of the concrete cylinders (diameter, length) and the total thickness of FRP layers, unconfined ultimate concrete strength, ultimate confinement pressure, ultimate tensile strength of the FRP laminates and the ultimate concrete strength of the concrete cylinders. The confined ultimate concrete strength is considered as the output data, while other parameters are considered as the input data. These parameters are mostly used in existing FRP-confined concrete models. Soft computing techniques are used to estimate the compressive strength of FRP-confined concrete cylinders. Finally, a new formulation is proposed. The results of the proposed formula are compared to the existing methods. To verify the proposed method, results are compared with other methods. The results show that the described method can forecast the compressive strength of FRP-confined concrete cylinders with high precision in comparison with the existing formulas. Moreover, the mean percentage of error for the proposed method is very low (3.49%). Furthermore, the proposed formula can estimate the ultimate compressive capacity of FRP-confined concrete cylinders with a different type of FRP and arbitrary thickness in the initial design of practical projects. |
topic |
frp soft computing compressive strength confined concrete artificial neural network |
url |
https://www.mdpi.com/2076-3417/10/5/1769 |
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