An Optimized Neuro-Bee Algorithm Approach to Predict the FRP-Concrete Bond Strength of RC Beams
Kumar, Aman ; Arora, Harish Chandra ; Mohammed, Mazin Abed ; Kumar, Krishna LU
and Nedoma, Jan
(2022)
In IEEE Access
10.
p.3790-3806
- Abstract
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... (More)
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.
(Less)
- author
- Kumar, Aman
; Arora, Harish Chandra
; Mohammed, Mazin Abed
; Kumar, Krishna
LU
and Nedoma, Jan
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ABC-ANN, ANN, Bond strength, FRP, FRP-concrete bond, Machine leaning
- in
- IEEE Access
- volume
- 10
- pages
- 17 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85122576326
- ISSN
- 2169-3536
- DOI
- 10.1109/ACCESS.2021.3140046
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2013 IEEE.
- id
- f08db891-e0b7-45f6-8619-964bee6ee4e9
- date added to LUP
- 2024-04-15 12:55:00
- date last changed
- 2024-05-22 09:04:18
@article{f08db891-e0b7-45f6-8619-964bee6ee4e9,
abstract = {{<p>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.</p>}},
author = {{Kumar, Aman and Arora, Harish Chandra and Mohammed, Mazin Abed and Kumar, Krishna and Nedoma, Jan}},
issn = {{2169-3536}},
keywords = {{ABC-ANN; ANN; Bond strength; FRP; FRP-concrete bond; Machine leaning}},
language = {{eng}},
pages = {{3790--3806}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Access}},
title = {{An Optimized Neuro-Bee Algorithm Approach to Predict the FRP-Concrete Bond Strength of RC Beams}},
url = {{http://dx.doi.org/10.1109/ACCESS.2021.3140046}},
doi = {{10.1109/ACCESS.2021.3140046}},
volume = {{10}},
year = {{2022}},
}