Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks
Singh, Sharanjit ; Arora, Harish Chandra ; Kumar, Aman ; Kapoor, Nishant Raj ; Onyelowe, Kennedy C. ; Kumar, Krishna LU
and Rai, Hardeep Singh
(2023)
In Advances in Materials Science and Engineering
2023.
- Abstract
Cement manufacturing and utilization is one of the majorly responsible factors for global CO2 emissions. In light of sustainability and climate change concerns, it is essential to find alternative solutions to reduce the carbon footprint of cement. Secondary cementitious materials (SCMs) are helpful in reducing carbon emissions from concrete. One such solution is the use of agricultural waste as SCMs to reduce carbon emissions from concrete. Especially rice husk ash (RHA) is a silica-rich, globally available agricultural waste material. The compressive strength (CS) of concrete is important and is used to evaluate the material's strength and durability. Predicting CS using a laboratory method is a costly, time-consuming, and complex... (More)
Cement manufacturing and utilization is one of the majorly responsible factors for global CO2 emissions. In light of sustainability and climate change concerns, it is essential to find alternative solutions to reduce the carbon footprint of cement. Secondary cementitious materials (SCMs) are helpful in reducing carbon emissions from concrete. One such solution is the use of agricultural waste as SCMs to reduce carbon emissions from concrete. Especially rice husk ash (RHA) is a silica-rich, globally available agricultural waste material. The compressive strength (CS) of concrete is important and is used to evaluate the material's strength and durability. Predicting CS using a laboratory method is a costly, time-consuming, and complex process. ML-based prediction models are the modern solution to these problems. In this study, a total of 407 datasets are used to develop an ML-based model by using the ANN algorithm to predict the CS of concrete containing RHA. Cement, coarse aggregates, fine aggregates, water, rice husk ash, superplasticizer, and type of sample are used as input parameters to predict CS at 28 days. Various statistical parameters including correlation coefficient (R), root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe (NS), and the a20-index have been used to assess the performance of the developed ANN model. The R and RMSE values of training, validation, and testing samples are 0.9928, 0.9864, and 0.9545, and 1.6471 MPa, 2.7149 MPa, and 4.4334 MPa, respectively. The results obtained from this study have been found to be promising and enrich the available literature. This work will nudge civil engineering and material science researchers toward opting for sustainable computing techniques. However, the study's limitations include the need for additional research into the material's long-term behaviour as well as the consideration of other characteristics that may affect its strength, such as environmental conditions like temperature and humidity.
(Less)
- author
- Singh, Sharanjit
; Arora, Harish Chandra
; Kumar, Aman
; Kapoor, Nishant Raj
; Onyelowe, Kennedy C.
; Kumar, Krishna
LU
and Rai, Hardeep Singh
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Advances in Materials Science and Engineering
- volume
- 2023
- article number
- 1177458
- publisher
- Hindawi Limited
- external identifiers
-
- scopus:85165697600
- ISSN
- 1687-8434
- DOI
- 10.1155/2023/1177458
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2023 Sharanjit Singh et al.
- id
- 35a8521f-c9ca-42a4-8cde-fbb400d224de
- date added to LUP
- 2024-04-15 13:30:47
- date last changed
- 2024-05-22 09:04:19
@article{35a8521f-c9ca-42a4-8cde-fbb400d224de,
abstract = {{<p>Cement manufacturing and utilization is one of the majorly responsible factors for global CO2 emissions. In light of sustainability and climate change concerns, it is essential to find alternative solutions to reduce the carbon footprint of cement. Secondary cementitious materials (SCMs) are helpful in reducing carbon emissions from concrete. One such solution is the use of agricultural waste as SCMs to reduce carbon emissions from concrete. Especially rice husk ash (RHA) is a silica-rich, globally available agricultural waste material. The compressive strength (CS) of concrete is important and is used to evaluate the material's strength and durability. Predicting CS using a laboratory method is a costly, time-consuming, and complex process. ML-based prediction models are the modern solution to these problems. In this study, a total of 407 datasets are used to develop an ML-based model by using the ANN algorithm to predict the CS of concrete containing RHA. Cement, coarse aggregates, fine aggregates, water, rice husk ash, superplasticizer, and type of sample are used as input parameters to predict CS at 28 days. Various statistical parameters including correlation coefficient (R), root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe (NS), and the a20-index have been used to assess the performance of the developed ANN model. The R and RMSE values of training, validation, and testing samples are 0.9928, 0.9864, and 0.9545, and 1.6471 MPa, 2.7149 MPa, and 4.4334 MPa, respectively. The results obtained from this study have been found to be promising and enrich the available literature. This work will nudge civil engineering and material science researchers toward opting for sustainable computing techniques. However, the study's limitations include the need for additional research into the material's long-term behaviour as well as the consideration of other characteristics that may affect its strength, such as environmental conditions like temperature and humidity.</p>}},
author = {{Singh, Sharanjit and Arora, Harish Chandra and Kumar, Aman and Kapoor, Nishant Raj and Onyelowe, Kennedy C. and Kumar, Krishna and Rai, Hardeep Singh}},
issn = {{1687-8434}},
language = {{eng}},
publisher = {{Hindawi Limited}},
series = {{Advances in Materials Science and Engineering}},
title = {{Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks}},
url = {{http://dx.doi.org/10.1155/2023/1177458}},
doi = {{10.1155/2023/1177458}},
volume = {{2023}},
year = {{2023}},
}