An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions
Growing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high...
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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/10.1155/2019/3831813 |
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| author | Sehmus Fidan Hasan Oktay Suleyman Polat Sarper Ozturk |
| author_facet | Sehmus Fidan Hasan Oktay Suleyman Polat Sarper Ozturk |
| author_sort | Sehmus Fidan |
| collection | DOAJ |
| description | Growing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high accuracy and precision tests are required for its determination which increases time and experimental costs. The main scope of this study is to develop a model based on artificial neural network (ANN) in order to predict the thermal properties of concrete through its mechanical characteristics. Initially, different concrete samples were prepared, and their both mechanical and thermal properties were tested in accordance with ASTM and EN standards. Then, the Levenberg–Marquardt algorithm was used for training the neural network in the single hidden layer using 5, 10, 15, 20, and 25 neurons, respectively. For each thermal property, various activation functions such as tangent sigmoid functions and triangular basis functions were used to examine the best solution performance. Moreover, a cross-validation technique was used to ensure good generalization and to avoid overtraining. ANN results showed that the best overall R2 performances for the prediction of thermal conductivity, specific heat, and thermal diffusivity were obtained as 0.996, 0.983, and 0.995 for tansig activation functions with 25, 25, and 20 neurons, respectively. The performance results showed that there was a great consistency between the predicted and tested results, demonstrating the feasibility and practicability of the proposed ANN models for predicting the thermal property of a concrete. |
| format | Article |
| id | doaj-art-dbf6717ba9b54da78d596a5dbc30bb1f |
| 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-dbf6717ba9b54da78d596a5dbc30bb1f2025-02-03T01:24:20ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422019-01-01201910.1155/2019/38318133831813An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation FunctionsSehmus Fidan0Hasan Oktay1Suleyman Polat2Sarper Ozturk3Department of Electrical and Electronic Engineering, Batman University, Batman 72100, TurkeyDepartment of Mechanical Engineering, Batman University, Batman 72100, TurkeyDepartment of Geological Engineering, Batman University, Batman 72100, TurkeyDepartment of Petroleum Engineering, Azerbaijan State Oil Academy, Baku 1000, AzerbaijanGrowing concerns on energy consumption of buildings by heating and cooling applications have led to a demand for improved insulating performances of building materials. The establishment of thermal property for a building structure is the key performance indicator for energy efficiency, whereas high accuracy and precision tests are required for its determination which increases time and experimental costs. The main scope of this study is to develop a model based on artificial neural network (ANN) in order to predict the thermal properties of concrete through its mechanical characteristics. Initially, different concrete samples were prepared, and their both mechanical and thermal properties were tested in accordance with ASTM and EN standards. Then, the Levenberg–Marquardt algorithm was used for training the neural network in the single hidden layer using 5, 10, 15, 20, and 25 neurons, respectively. For each thermal property, various activation functions such as tangent sigmoid functions and triangular basis functions were used to examine the best solution performance. Moreover, a cross-validation technique was used to ensure good generalization and to avoid overtraining. ANN results showed that the best overall R2 performances for the prediction of thermal conductivity, specific heat, and thermal diffusivity were obtained as 0.996, 0.983, and 0.995 for tansig activation functions with 25, 25, and 20 neurons, respectively. The performance results showed that there was a great consistency between the predicted and tested results, demonstrating the feasibility and practicability of the proposed ANN models for predicting the thermal property of a concrete.http://dx.doi.org/10.1155/2019/3831813 |
| spellingShingle | Sehmus Fidan Hasan Oktay Suleyman Polat Sarper Ozturk An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions Advances in Materials Science and Engineering |
| title | An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions |
| title_full | An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions |
| title_fullStr | An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions |
| title_full_unstemmed | An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions |
| title_short | An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions |
| title_sort | artificial neural network model to predict the thermal properties of concrete using different neurons and activation functions |
| url | http://dx.doi.org/10.1155/2019/3831813 |
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