Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network
The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/2608231 |
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| author | Kraiwut Tuntisukrarom Raungrut Cheerarot |
| author_facet | Kraiwut Tuntisukrarom Raungrut Cheerarot |
| author_sort | Kraiwut Tuntisukrarom |
| collection | DOAJ |
| description | The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results. |
| format | Article |
| id | doaj-art-07cc8b34590d45429537680593e0c7eb |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-07cc8b34590d45429537680593e0c7eb2025-02-03T06:06:42ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422020-01-01202010.1155/2020/26082312608231Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural NetworkKraiwut Tuntisukrarom0Raungrut Cheerarot1Concrete and Computer Research Unit, Civil Engineering, Faculty of Engineering, Mahasarakham University, Kantharawichai, Mahasarakham 44150, ThailandConcrete and Computer Research Unit, Civil Engineering, Faculty of Engineering, Mahasarakham University, Kantharawichai, Mahasarakham 44150, ThailandThe objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results.http://dx.doi.org/10.1155/2020/2608231 |
| spellingShingle | Kraiwut Tuntisukrarom Raungrut Cheerarot Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network Advances in Materials Science and Engineering |
| title | Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network |
| title_full | Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network |
| title_fullStr | Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network |
| title_full_unstemmed | Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network |
| title_short | Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network |
| title_sort | prediction of compressive strength behavior of ground bottom ash concrete by an artificial neural network |
| url | http://dx.doi.org/10.1155/2020/2608231 |
| work_keys_str_mv | AT kraiwuttuntisukrarom predictionofcompressivestrengthbehaviorofgroundbottomashconcretebyanartificialneuralnetwork AT raungrutcheerarot predictionofcompressivestrengthbehaviorofgroundbottomashconcretebyanartificialneuralnetwork |