An Optimized Neuro-Bee Algorithm Approach to Predict the FRP-Concrete Bond Strength of RC Beams
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10248795" target="_blank" >RIV/61989100:27240/22:10248795 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9667386" target="_blank" >https://ieeexplore.ieee.org/document/9667386</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2021.3140046" target="_blank" >10.1109/ACCESS.2021.3140046</a>
Alternative languages
Result language
angličtina
Original language name
An Optimized Neuro-Bee Algorithm Approach to Predict the FRP-Concrete Bond Strength of RC Beams
Original language description
Over the world, there is growing worry about the corrosion of reinforced concrete structures. Structure repair, rehabilitation, replacement, and new structures all require cost-effective and long-lasting technologies. Fiber Reinforced Polymer (FRP) has been widely employed in both retrofitting existing structures and building new ones. Due to its varied qualities in reinforced concrete and masonry constructions as a repair composite material, FRP have seen a rise in use over the last decade. This material have several advantages such as high stiffness-to-weight and strength-to-weight ratios, light weight, possibly high longevity, and relative ease of usage in the field. Among all the parameters the bond between concrete and FRP composite play an important role in the strengthening of structures. However, the bond behaviour of the FRP-concrete interface is complex, with several failure modes, making the bond strength difficult to forecast, resulting in the FRP strengthened concrete structure. To overcome such kind of issues machine learning models are sufficient to forecast the bond strength of FRP-concrete. In this article Artificial Neural Network (ANN), optimized Artificial Bee Colony (ABC)-ANN and Gaussian Process Regression (GPR) algorithms are deployed to predict the bond strength. The R-value of ABC-ANN and GPR models are 0.9514 and 0.9618 respectively. This research aids researchers in estimating bond strength in less time, at a lower cost, and with less experimental work. Author
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
10
Issue of the periodical within the volume
2022
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
3790-3806
UT code for WoS article
000742699400001
EID of the result in the Scopus database
2-s2.0-85122576326