Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks
Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical c...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/849126 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568259387326464 |
|---|---|
| author | Mehdi Nikoo Farshid Torabian Moghadam Łukasz Sadowski |
| author_facet | Mehdi Nikoo Farshid Torabian Moghadam Łukasz Sadowski |
| author_sort | Mehdi Nikoo |
| collection | DOAJ |
| description | Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete. |
| format | Article |
| id | doaj-art-fb1dabd3c79a4ae0b897c76a3b344709 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-fb1dabd3c79a4ae0b897c76a3b3447092025-02-03T00:59:28ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422015-01-01201510.1155/2015/849126849126Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural NetworksMehdi Nikoo0Farshid Torabian Moghadam1Łukasz Sadowski2Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, IranYoung Researchers and Elite Club, khorasgan (Isfahan) Branch, Islamic Azad University, Isfahan, IranFaculty of Civil Engineering, Wrocław University of Technology, Wybrzeze Wyspiańskiego 27, 50-370 Wrocław, PolandCompressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.http://dx.doi.org/10.1155/2015/849126 |
| spellingShingle | Mehdi Nikoo Farshid Torabian Moghadam Łukasz Sadowski Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks Advances in Materials Science and Engineering |
| title | Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks |
| title_full | Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks |
| title_fullStr | Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks |
| title_full_unstemmed | Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks |
| title_short | Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks |
| title_sort | prediction of concrete compressive strength by evolutionary artificial neural networks |
| url | http://dx.doi.org/10.1155/2015/849126 |
| work_keys_str_mv | AT mehdinikoo predictionofconcretecompressivestrengthbyevolutionaryartificialneuralnetworks AT farshidtorabianmoghadam predictionofconcretecompressivestrengthbyevolutionaryartificialneuralnetworks AT łukaszsadowski predictionofconcretecompressivestrengthbyevolutionaryartificialneuralnetworks |