Development of bond strength model for FRP plates using back-propagation algorithm

For the flexural reinforcement of bridge and building structure, synthetic materials whose dynamic properties are superior and those containing the merit of corrosion‐proof are widely used as the substitute for a steel plate. Since FRP plate has improved bond strength owing to the fibers externally...

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Bibliographic Details
Published in:Journal of applied polymer science 2006-06, Vol.100 (6), p.5119-5127
Main Authors: Park, Do Kyong, Jang, Hwasup, Ahn, Namshik
Format: Article
Language:English
Subjects:
Application fields
Applied sciences
back-propagation algorithm
bond strength
Buildings. Public works
Concretes. Mortars. Grouts
Exact sciences and technology
FRP
Materials
neural network
Plastics
Polymer industry, paints, wood
Technology of polymers
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Summary:For the flexural reinforcement of bridge and building structure, synthetic materials whose dynamic properties are superior and those containing the merit of corrosion‐proof are widely used as the substitute for a steel plate. Since FRP plate has improved bond strength owing to the fibers externally adhering to the plate, many researches regarding the bond strength improvement have been substantially performed. To search out such bond strength improvement, previous researchers had ever examined the bond strength of FRP plate through their experiment by setting up many variables. However, since the experiment for a research on the bond strength takes much of expenditure for setting up the equipment and is time‐consuming, also is difficult to be carried out, it is limitedly conducted. The purpose of this study was to develop the most suitable artificial neural network model by application of various neural network models and algorithm to the data of the bond strength experiment conducted by previous researchers. Many variables were used as input layers against bond strength: depth, width, modulus of elasticity, tensile strength of FRP plate and the compressive strength, tensile strength, and width of concrete. The developed artificial neural network model has been applied back‐propagation, and its error was learned to be converged within the range of 0.001. Besides, the process for the over‐fitting problem has been dissolved by Bayesian technique. The verification on the developed model was executed by comparison with the test results of bond strength made by other previous researchers, which was never been utilized to the learning as yet. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 5119–5127, 2006
ISSN:0021-8995
1097-4628
DOI:10.1002/app.24069