Axial Capacity of FRP-Reinforced Concrete Columns : Computational Intelligence-Based Prognosis for Sustainable Structures
Arora, Harish Chandra ; Kumar, Sourav ; Kontoni, Denise Penelope N. ; Kumar, Aman ; Sharma, Madhu ; Kapoor, Nishant Raj and Kumar, Krishna LU
(2022)
In Buildings
12(12).
- Abstract
Due to the corrosion problem in reinforced concrete structures, the use of fiber-reinforced polymer (FRP) bars may be preferred in place of traditional reinforcing steel. FRP bars are used in concrete constructions to boost the strength of structural elements and retain their longevity. In this study, the axial load carrying capacity (ALCC) of the FRP-reinforced concrete columns has been evaluated using analytical, as well as machine learning, models. A total of fourteen popular analytical models and one proposed machine learning-based model were used to estimate the ALCC of the concrete columns. The proposed machine learning model is based on an artificial neural network (ANN) method. The performance of the ANN, as well as the... (More)
Due to the corrosion problem in reinforced concrete structures, the use of fiber-reinforced polymer (FRP) bars may be preferred in place of traditional reinforcing steel. FRP bars are used in concrete constructions to boost the strength of structural elements and retain their longevity. In this study, the axial load carrying capacity (ALCC) of the FRP-reinforced concrete columns has been evaluated using analytical, as well as machine learning, models. A total of fourteen popular analytical models and one proposed machine learning-based model were used to estimate the ALCC of the concrete columns. The proposed machine learning model is based on an artificial neural network (ANN) method. The performance of the ANN, as well as the analytical models, are assessed using six different performance indices. The R-value of the developed ANN model is 0.9758, followed by an NS value of 0.9513. It has been found that the mean absolute percentage error of the best-fitted analytical model is 328.71% higher than the ANN model, and the root-mean-square error value of the best-fitted analytical model is 211.97% higher than the ANN model. The evaluated data demonstrate that the proposed ANN model performs better than the other analytical models. The developed model is quick and easy-to-use to estimate the axial capacity of the FRP-reinforced concrete columns.
(Less)
- author
- Arora, Harish Chandra
; Kumar, Sourav
; Kontoni, Denise Penelope N.
; Kumar, Aman
; Sharma, Madhu
; Kapoor, Nishant Raj
and Kumar, Krishna
LU
- publishing date
- 2022-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial neural network (ANN), axial capacity, concrete columns, fiber-reinforced polymer (FRP) bars, machine learning (ML)
- in
- Buildings
- volume
- 12
- issue
- 12
- article number
- 2137
- publisher
- MDPI AG
- external identifiers
-
- scopus:85144857448
- ISSN
- 2075-5309
- DOI
- 10.3390/buildings12122137
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 by the authors.
- id
- 885542fd-0b25-45aa-a5e8-e4fdf0d451e3
- date added to LUP
- 2024-04-15 13:18:19
- date last changed
- 2024-04-19 15:19:56
@article{885542fd-0b25-45aa-a5e8-e4fdf0d451e3,
abstract = {{<p>Due to the corrosion problem in reinforced concrete structures, the use of fiber-reinforced polymer (FRP) bars may be preferred in place of traditional reinforcing steel. FRP bars are used in concrete constructions to boost the strength of structural elements and retain their longevity. In this study, the axial load carrying capacity (ALCC) of the FRP-reinforced concrete columns has been evaluated using analytical, as well as machine learning, models. A total of fourteen popular analytical models and one proposed machine learning-based model were used to estimate the ALCC of the concrete columns. The proposed machine learning model is based on an artificial neural network (ANN) method. The performance of the ANN, as well as the analytical models, are assessed using six different performance indices. The R-value of the developed ANN model is 0.9758, followed by an NS value of 0.9513. It has been found that the mean absolute percentage error of the best-fitted analytical model is 328.71% higher than the ANN model, and the root-mean-square error value of the best-fitted analytical model is 211.97% higher than the ANN model. The evaluated data demonstrate that the proposed ANN model performs better than the other analytical models. The developed model is quick and easy-to-use to estimate the axial capacity of the FRP-reinforced concrete columns.</p>}},
author = {{Arora, Harish Chandra and Kumar, Sourav and Kontoni, Denise Penelope N. and Kumar, Aman and Sharma, Madhu and Kapoor, Nishant Raj and Kumar, Krishna}},
issn = {{2075-5309}},
keywords = {{artificial neural network (ANN); axial capacity; concrete columns; fiber-reinforced polymer (FRP) bars; machine learning (ML)}},
language = {{eng}},
number = {{12}},
publisher = {{MDPI AG}},
series = {{Buildings}},
title = {{Axial Capacity of FRP-Reinforced Concrete Columns : Computational Intelligence-Based Prognosis for Sustainable Structures}},
url = {{http://dx.doi.org/10.3390/buildings12122137}},
doi = {{10.3390/buildings12122137}},
volume = {{12}},
year = {{2022}},
}