Evaluation of Shear Capacity of FRP Reinforced Concrete Beams Using Artificial Neural Networks
Document Type
Article
Publication Date
2006
Publication Title
Smart Structures and Systems
Volume
2
Issue
1
First page number:
81
Last page number:
100
Abstract
To calculate the shear capacity of concrete beams reinforced with fibre-reinforced polymer (FRP), current shear design provisions use slightly modified versions of existing semi-empirical shear design equations that were primarily derived from experimental data generated on concrete beams having steel reinforcement. However, FRP materials have different mechanical properties and mode of failure than steel, and extending existing shear design equations for steel reinforced beams to cover concrete beams reinforced with FRP is questionable. This paper investigates the feasibility of using artificial neural networks (ANNs) to estimate the nominal shear capacity, Vn of concrete beams reinforced with FRP bars. Experimental data on 150 FRP-reinforced beams were retrieved from published literature. The resulting database was used to evaluate the validity of several existing shear design methods for FRP reinforced beams, namely the ACI 440-03, CSA S806-02, JSCE-97, and ISIS Canada-01. The database was also used to develop an ANN model to predict the shear capacity of FRP reinforced concrete beams. Results show that current guidelines are either inadequate or very conservative in estimating the shear strength of FRP reinforced concrete beams. Based on ANN predictions, modified equations are proposed for the shear design of FRP reinforced concrete beams and proved to be more accurate than existing equations.
Keywords
Concrete; Concrete beams; Fibre-reinforced polymer; Neural networks; Neural networks (Computer science); RC beams; Reinforced concrete; Shear (Mechanics); Shear strength
Disciplines
Civil and Environmental Engineering | Construction Engineering and Management | Structural Engineering
Language
English
Permissions
Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.
Repository Citation
El Chabib, H.,
Nehdi, M.,
Said, A.
(2006).
Evaluation of Shear Capacity of FRP Reinforced Concrete Beams Using Artificial Neural Networks.
Smart Structures and Systems, 2(1),
81-100.
http://dx.doi.org/10.12989/sss.2006.2.1.082