Enhancing Sustainability of Corroded RC Structures : Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms
Singh, Rohan ; Arora, Harish Chandra ; Bahrami, Alireza ; Kumar, Aman ; Kapoor, Nishant Raj ; Kumar, Krishna LU
and Rai, Hardeep Singh
(2022)
In Materials
15(23).
- Abstract
The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond strength, as they are built using various assumptions and limited datasets. The machine learning (ML) techniques named artificial neural network (ANN) and support vector machine (SVM) have been used to estimate the bond strength between concrete and corroded steel reinforcement bar. The considered input parameters in this research are the surface... (More)
The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond strength, as they are built using various assumptions and limited datasets. The machine learning (ML) techniques named artificial neural network (ANN) and support vector machine (SVM) have been used to estimate the bond strength between concrete and corroded steel reinforcement bar. The considered input parameters in this research are the surface area of the specimen, concrete cover, type of reinforcement bars, yield strength of reinforcement bars, concrete compressive strength, diameter of reinforcement bars, bond length, water/cement ratio, and corrosion level of reinforcement bars. These parameters were used to build the ANN and SVM models. The reliability of the developed ANN and SVM models have been compared with twenty analytical models. Moreover, the analyzed results revealed that the precision and efficiency of the ANN and SVM models are higher compared with the analytical models. The radar plot and Taylor diagrams have also been utilized to show the graphical representation of the best-fitted model. The proposed ANN model has the best precision and reliability compared with the SVM model, with a correlation coefficient of 0.99, mean absolute error of 1.091 MPa, and root mean square error of 1.495 MPa. Researchers and designers can apply the developed ANN model to precisely estimate the steel-to-concrete bond strength.
(Less)
- author
- Singh, Rohan
; Arora, Harish Chandra
; Bahrami, Alireza
; Kumar, Aman
; Kapoor, Nishant Raj
; Kumar, Krishna
LU
and Rai, Hardeep Singh
- publishing date
- 2022-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial neural network, bond strength, corroded steel reinforcement, corrosion, machine learning, reinforced concrete, support vector machine, sustainability
- in
- Materials
- volume
- 15
- issue
- 23
- article number
- 8295
- publisher
- MDPI AG
- external identifiers
-
- scopus:85143805352
- ISSN
- 1996-1944
- DOI
- 10.3390/ma15238295
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 by the authors.
- id
- 0d754484-85af-4e16-90b2-0fdcf4c5906f
- date added to LUP
- 2024-04-15 13:01:46
- date last changed
- 2024-04-19 15:10:28
@article{0d754484-85af-4e16-90b2-0fdcf4c5906f,
abstract = {{<p>The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond strength, as they are built using various assumptions and limited datasets. The machine learning (ML) techniques named artificial neural network (ANN) and support vector machine (SVM) have been used to estimate the bond strength between concrete and corroded steel reinforcement bar. The considered input parameters in this research are the surface area of the specimen, concrete cover, type of reinforcement bars, yield strength of reinforcement bars, concrete compressive strength, diameter of reinforcement bars, bond length, water/cement ratio, and corrosion level of reinforcement bars. These parameters were used to build the ANN and SVM models. The reliability of the developed ANN and SVM models have been compared with twenty analytical models. Moreover, the analyzed results revealed that the precision and efficiency of the ANN and SVM models are higher compared with the analytical models. The radar plot and Taylor diagrams have also been utilized to show the graphical representation of the best-fitted model. The proposed ANN model has the best precision and reliability compared with the SVM model, with a correlation coefficient of 0.99, mean absolute error of 1.091 MPa, and root mean square error of 1.495 MPa. Researchers and designers can apply the developed ANN model to precisely estimate the steel-to-concrete bond strength.</p>}},
author = {{Singh, Rohan and Arora, Harish Chandra and Bahrami, Alireza and Kumar, Aman and Kapoor, Nishant Raj and Kumar, Krishna and Rai, Hardeep Singh}},
issn = {{1996-1944}},
keywords = {{artificial neural network; bond strength; corroded steel reinforcement; corrosion; machine learning; reinforced concrete; support vector machine; sustainability}},
language = {{eng}},
number = {{23}},
publisher = {{MDPI AG}},
series = {{Materials}},
title = {{Enhancing Sustainability of Corroded RC Structures : Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms}},
url = {{http://dx.doi.org/10.3390/ma15238295}},
doi = {{10.3390/ma15238295}},
volume = {{15}},
year = {{2022}},
}