Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing Techniques
Agricultural waste materials are increasingly being used as partial replacements for cement in concrete. Several experimental studies are available to evaluate the mechanical properties of plastic waste reinforced concrete but there are limited evaluations on agricultural waste material. In this stu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Pouyan Press
2023-04-01
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| Series: | Journal of Soft Computing in Civil Engineering |
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| Online Access: | https://www.jsoftcivil.com/article_168920_2b51a9342cfebf2d6732620b68d76246.pdf |
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| author | R Abhishek B S Keerthi Gowda D C Naveen K Naresh R Sundarakannan V Arumugaprabu Amogha Varsha |
| author_facet | R Abhishek B S Keerthi Gowda D C Naveen K Naresh R Sundarakannan V Arumugaprabu Amogha Varsha |
| author_sort | R Abhishek |
| collection | DOAJ |
| description | Agricultural waste materials are increasingly being used as partial replacements for cement in concrete. Several experimental studies are available to evaluate the mechanical properties of plastic waste reinforced concrete but there are limited evaluations on agricultural waste material. In this study, an attempt is made to investigate the compressive strength of Corn Cob Ash (CCA) concrete at different replacement levels by implementing an Artificial Neural Network (ANN). As the percentage of CCA increases, workability, density and compressive strength decreases, hence the developed ANN model consists of 3 input parameters (cement content, CCA content, and curing ages) in the input layer, 4 hidden neurons in the hidden layer and 3 output parameters (slump, density, and compressive strength) in the output layer. Training is done by adopting Levenberg-Marquardt back-propagation algorithm by considering 80% of experimental data with log-sigmoid activation function for both hidden and output layers. The developed model has a high correlation coefficient of 0.999 for both the training and testing data sets. It has low MSE and MAPE values of 2.2768x10-5 and 1.25 for training data respectively and 3.0463x10-5 and 1.37 for testing data respectively. Hence, it is concluded that the developed model predicts the output at an average rate of 98% accuracy. The predicted 2.5% replaced CCA concrete shows the best performance at all curing ages. Therefore, this percentage level is considered as an optimum replacement level which does not much affect the hardened properties of concrete. |
| first_indexed | 2024-03-13T03:53:00Z |
| format | Article |
| id | doaj.art-8bd69a45a00e42b7bc0dc876b259377c |
| institution | Directory Open Access Journal |
| issn | 2588-2872 |
| language | English |
| last_indexed | 2024-03-13T03:53:00Z |
| publishDate | 2023-04-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj.art-8bd69a45a00e42b7bc0dc876b259377c2023-06-22T09:24:25ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722023-04-017211513710.22115/scce.2023.347663.1471168920Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing TechniquesR Abhishek0B S Keerthi Gowda1D C Naveen2K Naresh3R Sundarakannan4V Arumugaprabu5Amogha Varsha6Research Scholar, Department of Civil Engineering, Visvesvaraya Technological University, Mysuru, IndiaDepartment of Civil Engineering, Visvesvaraya Technological University, Mysuru, IndiaDepartment of Civil Engineering, Sri Venkateswara College of Engineering, Tirupati, IndiaDepartment of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi, U.A.E.Institute of Agricultural Engineering, Saveetha School of Engineering, SIMATS, Chennai, IndiaDepartment of Mechanical Engineering, Kalasalingam Academy of Research and Education, Tamilnadu, IndiaResearch Scholar, Department of Civil Engineering, PESCE (VTU), Mandya, Karnataka, IndiaAgricultural waste materials are increasingly being used as partial replacements for cement in concrete. Several experimental studies are available to evaluate the mechanical properties of plastic waste reinforced concrete but there are limited evaluations on agricultural waste material. In this study, an attempt is made to investigate the compressive strength of Corn Cob Ash (CCA) concrete at different replacement levels by implementing an Artificial Neural Network (ANN). As the percentage of CCA increases, workability, density and compressive strength decreases, hence the developed ANN model consists of 3 input parameters (cement content, CCA content, and curing ages) in the input layer, 4 hidden neurons in the hidden layer and 3 output parameters (slump, density, and compressive strength) in the output layer. Training is done by adopting Levenberg-Marquardt back-propagation algorithm by considering 80% of experimental data with log-sigmoid activation function for both hidden and output layers. The developed model has a high correlation coefficient of 0.999 for both the training and testing data sets. It has low MSE and MAPE values of 2.2768x10-5 and 1.25 for training data respectively and 3.0463x10-5 and 1.37 for testing data respectively. Hence, it is concluded that the developed model predicts the output at an average rate of 98% accuracy. The predicted 2.5% replaced CCA concrete shows the best performance at all curing ages. Therefore, this percentage level is considered as an optimum replacement level which does not much affect the hardened properties of concrete.https://www.jsoftcivil.com/article_168920_2b51a9342cfebf2d6732620b68d76246.pdfsoft-computing techniqueslevenberg-marquardtagriculture wasteconstruction materialspozzolanic material |
| spellingShingle | R Abhishek B S Keerthi Gowda D C Naveen K Naresh R Sundarakannan V Arumugaprabu Amogha Varsha Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing Techniques Journal of Soft Computing in Civil Engineering soft-computing techniques levenberg-marquardt agriculture waste construction materials pozzolanic material |
| title | Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing Techniques |
| title_full | Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing Techniques |
| title_fullStr | Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing Techniques |
| title_full_unstemmed | Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing Techniques |
| title_short | Prediction of Compressive Strength of Corncob Ash Concrete for Environmental Sustainability Using an Artificial Neural Network: A Soft Computing Techniques |
| title_sort | prediction of compressive strength of corncob ash concrete for environmental sustainability using an artificial neural network a soft computing techniques |
| topic | soft-computing techniques levenberg-marquardt agriculture waste construction materials pozzolanic material |
| url | https://www.jsoftcivil.com/article_168920_2b51a9342cfebf2d6732620b68d76246.pdf |
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