Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network
The present study is to compare the multiple regression analysis (MRA) model and artificial neural network (ANN) model designed to predict the mechanical strength of fiber-reinforced concrete on 28 days. The model uses the data from early literatures; the data consist of tensile strength of fiber, p...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/4654070 |
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| author | S. Karthiyaini K. Senthamaraikannan J. Priyadarshini Kamal Gupta M. Shanmugasundaram |
| author_facet | S. Karthiyaini K. Senthamaraikannan J. Priyadarshini Kamal Gupta M. Shanmugasundaram |
| author_sort | S. Karthiyaini |
| collection | DOAJ |
| description | The present study is to compare the multiple regression analysis (MRA) model and artificial neural network (ANN) model designed to predict the mechanical strength of fiber-reinforced concrete on 28 days. The model uses the data from early literatures; the data consist of tensile strength of fiber, percentage of fiber, water/cement ratio, cross-sectional area of test specimen, Young’s modulus of fiber, and mechanical strength of control specimen, and these were used as the input parameters; the respective strength attained was used as the target parameter. The models are created and are used to predict compressive, split tensile, and flexural strength of fiber admixed concrete. These models are evaluated through the statistical test such as coefficient of determination (R2) and root mean squared error (RMSE). The results show that these parameters produce a valid model through both MRA and ANN, and this model gives more precise prediction for the fiber admixed concrete. |
| format | Article |
| id | doaj-art-e15d6d6a55a4487b853832078f9e55a2 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-e15d6d6a55a4487b853832078f9e55a22025-02-03T01:01:37ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422019-01-01201910.1155/2019/46540704654070Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural NetworkS. Karthiyaini0K. Senthamaraikannan1J. Priyadarshini2Kamal Gupta3M. Shanmugasundaram4School of Mechanical and Building Sciences, Vellore Institute of Technology-Chennai Campus, Chennai-600127, Tamilnadu, IndiaDepartment of Civil and Architectural Engineering, Al Musanna College of Technology, Muladdah Musanna, OmanSchool of Computing Science and Engineering, Vellore Institute of Technology-Chennai Campus, Chennai-600127, Tamilnadu, IndiaSchool of Mechanical and Building Sciences, Vellore Institute of Technology-Chennai Campus, Chennai-600127, Tamilnadu, IndiaSchool of Mechanical and Building Sciences, Vellore Institute of Technology-Chennai Campus, Chennai-600127, Tamilnadu, IndiaThe present study is to compare the multiple regression analysis (MRA) model and artificial neural network (ANN) model designed to predict the mechanical strength of fiber-reinforced concrete on 28 days. The model uses the data from early literatures; the data consist of tensile strength of fiber, percentage of fiber, water/cement ratio, cross-sectional area of test specimen, Young’s modulus of fiber, and mechanical strength of control specimen, and these were used as the input parameters; the respective strength attained was used as the target parameter. The models are created and are used to predict compressive, split tensile, and flexural strength of fiber admixed concrete. These models are evaluated through the statistical test such as coefficient of determination (R2) and root mean squared error (RMSE). The results show that these parameters produce a valid model through both MRA and ANN, and this model gives more precise prediction for the fiber admixed concrete.http://dx.doi.org/10.1155/2019/4654070 |
| spellingShingle | S. Karthiyaini K. Senthamaraikannan J. Priyadarshini Kamal Gupta M. Shanmugasundaram Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network Advances in Materials Science and Engineering |
| title | Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network |
| title_full | Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network |
| title_fullStr | Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network |
| title_full_unstemmed | Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network |
| title_short | Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network |
| title_sort | prediction of mechanical strength of fiber admixed concrete using multiple regression analysis and artificial neural network |
| url | http://dx.doi.org/10.1155/2019/4654070 |
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