Development of Efficient Prediction Model of FRP-to-Concrete Bond Strength Using Curve Fitting and ANFIS Methods
Kumar, Aman ; Arora, Harish Chandra ; Kumar, Krishna LU
; Garg, Harish
and Jahangir, Hashem
(2024)
In Arabian Journal for Science and Engineering
49(4).
p.5129-5158
- Abstract
Externally bonded fiber-reinforced polymer (FRP) plates or sheets have become a common retrofitting approach for sustaining old reinforced concrete structures in the modern era. The capacity of FRP-strengthened structures cannot be accurately estimated because the bond strength between FRP and concrete surface is accurately unpredictable. Various studies are available in the literature to predict the FRP-to-concrete bond strength (FRP-CBS), but they are based on limited experimental data sets and have lesser accuracy. To solve this problem, curve-fitting (CF) and adaptive neuro-fuzzy inference systems (ANFIS) models have been developed to predict the FRP-CBS using 935 datasets. The database was collected from published literature and... (More)
Externally bonded fiber-reinforced polymer (FRP) plates or sheets have become a common retrofitting approach for sustaining old reinforced concrete structures in the modern era. The capacity of FRP-strengthened structures cannot be accurately estimated because the bond strength between FRP and concrete surface is accurately unpredictable. Various studies are available in the literature to predict the FRP-to-concrete bond strength (FRP-CBS), but they are based on limited experimental data sets and have lesser accuracy. To solve this problem, curve-fitting (CF) and adaptive neuro-fuzzy inference systems (ANFIS) models have been developed to predict the FRP-CBS using 935 datasets. The database was collected from published literature and the same was used to develop the ML model. Comparison with standard guidelines, including ACI, TR-55 fib, CNR, and JCI, and other analytical models, revealed that the ANFIS model outperformed the CF model and all other analytical models. The ANFIS model achieved a correlation coefficient of 0.9189 and a mean absolute error (MAE) of 2.43 kN, while the CF model achieved a correlation coefficient of 0.7303 and an MAE value of 4.30 kN. Moreover, a parametric study was conducted to identify the influence of each specific parameter on the bond strength. The developed ANFIS-based model can be readily utilized by structural engineers, FRP applicators, and researchers for estimating the FRP-to-concrete bond strength.
(Less)
- author
- Kumar, Aman
; Arora, Harish Chandra
; Kumar, Krishna
LU
; Garg, Harish
and Jahangir, Hashem
- publishing date
- 2024-04
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ANFIS, Bond strength prediction, Curve fitting, FRP-concrete interface, Fuzzy logic
- in
- Arabian Journal for Science and Engineering
- volume
- 49
- issue
- 4
- pages
- 30 pages
- publisher
- Springer
- external identifiers
-
- scopus:85174410011
- ISSN
- 2193-567X
- DOI
- 10.1007/s13369-023-08328-0
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © King Fahd University of Petroleum & Minerals 2023.
- id
- 001957a9-1e89-4029-a601-5f781fa362f7
- date added to LUP
- 2024-04-15 13:38:14
- date last changed
- 2024-05-16 14:46:49
@article{001957a9-1e89-4029-a601-5f781fa362f7,
abstract = {{<p>Externally bonded fiber-reinforced polymer (FRP) plates or sheets have become a common retrofitting approach for sustaining old reinforced concrete structures in the modern era. The capacity of FRP-strengthened structures cannot be accurately estimated because the bond strength between FRP and concrete surface is accurately unpredictable. Various studies are available in the literature to predict the FRP-to-concrete bond strength (FRP-CBS), but they are based on limited experimental data sets and have lesser accuracy. To solve this problem, curve-fitting (CF) and adaptive neuro-fuzzy inference systems (ANFIS) models have been developed to predict the FRP-CBS using 935 datasets. The database was collected from published literature and the same was used to develop the ML model. Comparison with standard guidelines, including ACI, TR-55 fib, CNR, and JCI, and other analytical models, revealed that the ANFIS model outperformed the CF model and all other analytical models. The ANFIS model achieved a correlation coefficient of 0.9189 and a mean absolute error (MAE) of 2.43 kN, while the CF model achieved a correlation coefficient of 0.7303 and an MAE value of 4.30 kN. Moreover, a parametric study was conducted to identify the influence of each specific parameter on the bond strength. The developed ANFIS-based model can be readily utilized by structural engineers, FRP applicators, and researchers for estimating the FRP-to-concrete bond strength.</p>}},
author = {{Kumar, Aman and Arora, Harish Chandra and Kumar, Krishna and Garg, Harish and Jahangir, Hashem}},
issn = {{2193-567X}},
keywords = {{ANFIS; Bond strength prediction; Curve fitting; FRP-concrete interface; Fuzzy logic}},
language = {{eng}},
number = {{4}},
pages = {{5129--5158}},
publisher = {{Springer}},
series = {{Arabian Journal for Science and Engineering}},
title = {{Development of Efficient Prediction Model of FRP-to-Concrete Bond Strength Using Curve Fitting and ANFIS Methods}},
url = {{http://dx.doi.org/10.1007/s13369-023-08328-0}},
doi = {{10.1007/s13369-023-08328-0}},
volume = {{49}},
year = {{2024}},
}