Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2023-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/1177458 |
| _version_ | 1797777510751010816 |
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| author | Sharanjit Singh Harish Chandra Arora Aman Kumar Nishant Raj Kapoor Kennedy C. Onyelowe Krishna Kumar Hardeep Singh Rai |
| author_facet | Sharanjit Singh Harish Chandra Arora Aman Kumar Nishant Raj Kapoor Kennedy C. Onyelowe Krishna Kumar Hardeep Singh Rai |
| author_sort | Sharanjit Singh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T23:05:58Z |
| format | Article |
| id | doaj.art-afe4235ed3324f198eb7851befddb41e |
| institution | Directory Open Access Journal |
| issn | 1687-8442 |
| language | English |
| last_indexed | 2024-03-12T23:05:58Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj.art-afe4235ed3324f198eb7851befddb41e2023-07-19T00:00:01ZengHindawi LimitedAdvances in Materials Science and Engineering1687-84422023-01-01202310.1155/2023/1177458Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural NetworksSharanjit Singh0Harish Chandra Arora1Aman Kumar2Nishant Raj Kapoor3Kennedy C. Onyelowe4Krishna Kumar5Hardeep Singh Rai6Civil EngineeringStructural Engineering DepartmentStructural Engineering DepartmentAcSIR-Academy of Scientific and Innovative ResearchDepartment of Civil EngineeringDepartment of Hydro and Renewable EnergyCivil EngineeringCement 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.http://dx.doi.org/10.1155/2023/1177458 |
| spellingShingle | Sharanjit Singh Harish Chandra Arora Aman Kumar Nishant Raj Kapoor Kennedy C. Onyelowe Krishna Kumar Hardeep Singh Rai Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks Advances in Materials Science and Engineering |
| title | Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks |
| title_full | Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks |
| title_fullStr | Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks |
| title_full_unstemmed | Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks |
| title_short | Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks |
| title_sort | 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 |
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