Practical applicable model for estimating the carbonation depth in fly-ash based concrete structures by utilizing adaptive neuro-fuzzy inference system
Kumar, Aman ; Arora, Harish Chandra ; Kapoor, Nishant Raj ; Kontoni, Denise Penelope N. ; Kumar, Krishna LU
; Jahangir, Hashem
and Bhushan, Bharat
(2023)
In Computers and Concrete
32(2).
p.119-138
- Abstract
Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce... (More)
Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.
(Less)
- author
- Kumar, Aman
; Arora, Harish Chandra
; Kapoor, Nishant Raj
; Kontoni, Denise Penelope N.
; Kumar, Krishna
LU
; Jahangir, Hashem
and Bhushan, Bharat
- publishing date
- 2023-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ANFIS method, carbonation depth, concrete properties, corrosion, fly-ash, machine learning techniques
- in
- Computers and Concrete
- volume
- 32
- issue
- 2
- pages
- 20 pages
- publisher
- Techno Press
- external identifiers
-
- scopus:85168315040
- ISSN
- 1598-8198
- DOI
- 10.12989/cac.2023.32.2.119
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: Copyright © 2023 Techno-Press, Ltd.
- id
- 6f78a586-f539-4855-b1f1-16d336ffd188
- date added to LUP
- 2024-04-16 09:16:50
- date last changed
- 2024-05-22 09:04:19
@article{6f78a586-f539-4855-b1f1-16d336ffd188,
abstract = {{<p>Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.</p>}},
author = {{Kumar, Aman and Arora, Harish Chandra and Kapoor, Nishant Raj and Kontoni, Denise Penelope N. and Kumar, Krishna and Jahangir, Hashem and Bhushan, Bharat}},
issn = {{1598-8198}},
keywords = {{ANFIS method; carbonation depth; concrete properties; corrosion; fly-ash; machine learning techniques}},
language = {{eng}},
number = {{2}},
pages = {{119--138}},
publisher = {{Techno Press}},
series = {{Computers and Concrete}},
title = {{Practical applicable model for estimating the carbonation depth in fly-ash based concrete structures by utilizing adaptive neuro-fuzzy inference system}},
url = {{http://dx.doi.org/10.12989/cac.2023.32.2.119}},
doi = {{10.12989/cac.2023.32.2.119}},
volume = {{32}},
year = {{2023}},
}