Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete
A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/5481705 |
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| _version_ | 1832548654123057152 |
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| author | Palika Chopra Rajendra Kumar Sharma Maneek Kumar Tanuj Chopra |
| author_facet | Palika Chopra Rajendra Kumar Sharma Maneek Kumar Tanuj Chopra |
| author_sort | Palika Chopra |
| collection | DOAJ |
| description | A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R2) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete. |
| format | Article |
| id | doaj-art-0d38979b2244426d9c5e8f42fcd13efd |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-0d38979b2244426d9c5e8f42fcd13efd2025-02-03T06:13:22ZengWileyAdvances in Civil Engineering1687-80861687-80942018-01-01201810.1155/2018/54817055481705Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of ConcretePalika Chopra0Rajendra Kumar Sharma1Maneek Kumar2Tanuj Chopra3Department of Computer Science and Engineering, Thapar University, Patiala, IndiaDepartment of Computer Science and Engineering, Thapar University, Patiala, IndiaDepartment of Civil Engineering, Thapar University, Patiala, IndiaDepartment of Civil Engineering, Thapar University, Patiala, IndiaA comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R2) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.http://dx.doi.org/10.1155/2018/5481705 |
| spellingShingle | Palika Chopra Rajendra Kumar Sharma Maneek Kumar Tanuj Chopra Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete Advances in Civil Engineering |
| title | Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete |
| title_full | Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete |
| title_fullStr | Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete |
| title_full_unstemmed | Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete |
| title_short | Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete |
| title_sort | comparison of machine learning techniques for the prediction of compressive strength of concrete |
| url | http://dx.doi.org/10.1155/2018/5481705 |
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