Prognosis of compressive strength of fly-ash-based geopolymer-modified sustainable concrete with ML algorithms
Kumar, Aman ; Arora, Harish Chandra ; Kapoor, Nishant Raj and Kumar, Krishna LU
(2023)
In Structural Concrete
24(3).
p.3990-4014
- Abstract
Sustainable concrete is the demand of the present era to reduce carbon emissions. Fly-ash-based geopolymer (FLAG) concrete has been used in the construction industry for more than one and a half decades. The compressive strength (CS) of concrete plays a crucial role in the mechanical properties of concrete. Laboratory experiments take a huge amount of time and cost to estimate the CS of concrete. Although analytical methods exist to estimate the CS of concrete, but these models cannot forecast the CS of concrete with better precision due to the complexity of the design mixes. The machine learning (ML)-based models have been helpful in estimating the CS of concrete with high accuracy and reliability. In this article, four ML algorithms... (More)
Sustainable concrete is the demand of the present era to reduce carbon emissions. Fly-ash-based geopolymer (FLAG) concrete has been used in the construction industry for more than one and a half decades. The compressive strength (CS) of concrete plays a crucial role in the mechanical properties of concrete. Laboratory experiments take a huge amount of time and cost to estimate the CS of concrete. Although analytical methods exist to estimate the CS of concrete, but these models cannot forecast the CS of concrete with better precision due to the complexity of the design mixes. The machine learning (ML)-based models have been helpful in estimating the CS of concrete with high accuracy and reliability. In this article, four ML algorithms (support vector machine [SVM], linear regression [LR], ensemble learning [EL], and Gaussian process regression [GPR]) and three optimized ML algorithms (EL, SVM, and GPR) have been used to estimate the CS of FLAG concrete. The R-value of LR, EL, SVMR, GPR, optimized EL, optimized SVMR and optimized GPR models are 0.8916, 0.9172, 0.9313, 0.9529, 0.9459, 0.9348 and 0.9590, respectively. The accuracy of the optimized GPR model with an R-value of 0.9590 and RMSE value of 1.7132 MPa outperformed all other ML models. The performances of all the developed models have been illustrated through Taylor diagram and error plot. The feature importance of the input parameters has been explained with the explainable ML technique. The developed, optimized GPR model can be reliable tool to estimate the CS with greater accuracy and also reducing time and cost.
(Less)
- author
- Kumar, Aman
; Arora, Harish Chandra
; Kapoor, Nishant Raj
and Kumar, Krishna
LU
- publishing date
- 2023-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- advance material, GPR, machine learning, modern concrete, sustainability, SVM
- in
- Structural Concrete
- volume
- 24
- issue
- 3
- pages
- 25 pages
- publisher
- Thomas Telford
- external identifiers
-
- scopus:85138016040
- ISSN
- 1464-4177
- DOI
- 10.1002/suco.202200344
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 fib. International Federation for Structural Concrete.
- id
- 53bacfbb-a4ed-49e6-b724-e04a8736849e
- date added to LUP
- 2024-04-15 13:00:44
- date last changed
- 2024-04-19 15:08:33
@article{53bacfbb-a4ed-49e6-b724-e04a8736849e,
abstract = {{<p>Sustainable concrete is the demand of the present era to reduce carbon emissions. Fly-ash-based geopolymer (FLAG) concrete has been used in the construction industry for more than one and a half decades. The compressive strength (CS) of concrete plays a crucial role in the mechanical properties of concrete. Laboratory experiments take a huge amount of time and cost to estimate the CS of concrete. Although analytical methods exist to estimate the CS of concrete, but these models cannot forecast the CS of concrete with better precision due to the complexity of the design mixes. The machine learning (ML)-based models have been helpful in estimating the CS of concrete with high accuracy and reliability. In this article, four ML algorithms (support vector machine [SVM], linear regression [LR], ensemble learning [EL], and Gaussian process regression [GPR]) and three optimized ML algorithms (EL, SVM, and GPR) have been used to estimate the CS of FLAG concrete. The R-value of LR, EL, SVMR, GPR, optimized EL, optimized SVMR and optimized GPR models are 0.8916, 0.9172, 0.9313, 0.9529, 0.9459, 0.9348 and 0.9590, respectively. The accuracy of the optimized GPR model with an R-value of 0.9590 and RMSE value of 1.7132 MPa outperformed all other ML models. The performances of all the developed models have been illustrated through Taylor diagram and error plot. The feature importance of the input parameters has been explained with the explainable ML technique. The developed, optimized GPR model can be reliable tool to estimate the CS with greater accuracy and also reducing time and cost.</p>}},
author = {{Kumar, Aman and Arora, Harish Chandra and Kapoor, Nishant Raj and Kumar, Krishna}},
issn = {{1464-4177}},
keywords = {{advance material; GPR; machine learning; modern concrete; sustainability; SVM}},
language = {{eng}},
number = {{3}},
pages = {{3990--4014}},
publisher = {{Thomas Telford}},
series = {{Structural Concrete}},
title = {{Prognosis of compressive strength of fly-ash-based geopolymer-modified sustainable concrete with ML algorithms}},
url = {{http://dx.doi.org/10.1002/suco.202200344}},
doi = {{10.1002/suco.202200344}},
volume = {{24}},
year = {{2023}},
}