Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods
Machine learning methods have been successfully applied to many engineering disciplines. Prediction of the concrete compressive strength (fc) and slump (S) is important in terms of the desirability of concrete and its sustainability. The goals of this study were (i) to determine the most successful...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/3069046 |
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| _version_ | 1849306697450913792 |
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| author | M. Timur Cihan |
| author_facet | M. Timur Cihan |
| author_sort | M. Timur Cihan |
| collection | DOAJ |
| description | Machine learning methods have been successfully applied to many engineering disciplines. Prediction of the concrete compressive strength (fc) and slump (S) is important in terms of the desirability of concrete and its sustainability. The goals of this study were (i) to determine the most successful normalization technique for the datasets, (ii) to select the prime regression method to predict the fc and S outputs, (iii) to obtain the best subset with the ReliefF feature selection method, and (iv) to compare the regression results for the original and selected subsets. Experimental results demonstrate that the decimal scaling and min-max normalization techniques are the most successful methods for predicting the compressive strength and slump outputs, respectively. According to the evaluation metrics, such as the correlation coefficient, root mean squared error, and mean absolute error, the fuzzy logic method makes better predictions than any other regression method. Moreover, when the input variable was reduced from seven to four by the ReliefF feature selection method, the predicted accuracy was within the acceptable error rate. |
| format | Article |
| id | doaj-art-26244a3761df4245ac63d38aba27a3cf |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-26244a3761df4245ac63d38aba27a3cf2025-08-20T03:54:58ZengWileyAdvances in Civil Engineering1687-80861687-80942019-01-01201910.1155/2019/30690463069046Prediction of Concrete Compressive Strength and Slump by Machine Learning MethodsM. Timur Cihan0Civil Engineering, Tekirdağ Namık Kemal University, Çorlu Faculty of Engineering, Tekirdağ 59860, TurkeyMachine learning methods have been successfully applied to many engineering disciplines. Prediction of the concrete compressive strength (fc) and slump (S) is important in terms of the desirability of concrete and its sustainability. The goals of this study were (i) to determine the most successful normalization technique for the datasets, (ii) to select the prime regression method to predict the fc and S outputs, (iii) to obtain the best subset with the ReliefF feature selection method, and (iv) to compare the regression results for the original and selected subsets. Experimental results demonstrate that the decimal scaling and min-max normalization techniques are the most successful methods for predicting the compressive strength and slump outputs, respectively. According to the evaluation metrics, such as the correlation coefficient, root mean squared error, and mean absolute error, the fuzzy logic method makes better predictions than any other regression method. Moreover, when the input variable was reduced from seven to four by the ReliefF feature selection method, the predicted accuracy was within the acceptable error rate.http://dx.doi.org/10.1155/2019/3069046 |
| spellingShingle | M. Timur Cihan Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods Advances in Civil Engineering |
| title | Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods |
| title_full | Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods |
| title_fullStr | Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods |
| title_full_unstemmed | Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods |
| title_short | Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods |
| title_sort | prediction of concrete compressive strength and slump by machine learning methods |
| url | http://dx.doi.org/10.1155/2019/3069046 |
| work_keys_str_mv | AT mtimurcihan predictionofconcretecompressivestrengthandslumpbymachinelearningmethods |