Predicting the Compressive Strength of Concrete Using an RBF-ANN Model
In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in the author’s previous work. The stochastic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/14/6382 |
| _version_ | 1797527699528351744 |
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| author | Nan-Jing Wu |
| author_facet | Nan-Jing Wu |
| author_sort | Nan-Jing Wu |
| collection | DOAJ |
| description | In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in the author’s previous work. The stochastic gradient approach presented in the textbook is employed for determining the centers of RBFs and their shape parameters. With an extremely large number of training iterations and just a few RBFs in the ANN, all the RBF-ANNs have converged to the solutions of global minimum error. So, the only consideration of whether the ANN can work in practical uses is just the issue of over-fitting. The ANN with only three RBFs is finally chosen. The results of verification imply that the present RBF-ANN model outperforms the BP-ANN model in the author’s previous work. The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. With these numbers and using the formulae expressed in this article, anyone can predict the 28-day compressive strength of concrete according to the concrete mix proportioning on his/her own. |
| first_indexed | 2024-03-10T09:47:28Z |
| format | Article |
| id | doaj.art-eb40b01758b0434e8f4e23ba65b4e9b9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:47:28Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-eb40b01758b0434e8f4e23ba65b4e9b92023-11-22T03:08:48ZengMDPI AGApplied Sciences2076-34172021-07-011114638210.3390/app11146382Predicting the Compressive Strength of Concrete Using an RBF-ANN ModelNan-Jing Wu0Department of Civil and Water Resources Engineering, National Chiayi University, Chiayi 699355, TaiwanIn this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in the author’s previous work. The stochastic gradient approach presented in the textbook is employed for determining the centers of RBFs and their shape parameters. With an extremely large number of training iterations and just a few RBFs in the ANN, all the RBF-ANNs have converged to the solutions of global minimum error. So, the only consideration of whether the ANN can work in practical uses is just the issue of over-fitting. The ANN with only three RBFs is finally chosen. The results of verification imply that the present RBF-ANN model outperforms the BP-ANN model in the author’s previous work. The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. With these numbers and using the formulae expressed in this article, anyone can predict the 28-day compressive strength of concrete according to the concrete mix proportioning on his/her own.https://www.mdpi.com/2076-3417/11/14/6382radial basis functionsartificial neural networksprediction modelcompressive strength of concretemix proportioning of concrete |
| spellingShingle | Nan-Jing Wu Predicting the Compressive Strength of Concrete Using an RBF-ANN Model Applied Sciences radial basis functions artificial neural networks prediction model compressive strength of concrete mix proportioning of concrete |
| title | Predicting the Compressive Strength of Concrete Using an RBF-ANN Model |
| title_full | Predicting the Compressive Strength of Concrete Using an RBF-ANN Model |
| title_fullStr | Predicting the Compressive Strength of Concrete Using an RBF-ANN Model |
| title_full_unstemmed | Predicting the Compressive Strength of Concrete Using an RBF-ANN Model |
| title_short | Predicting the Compressive Strength of Concrete Using an RBF-ANN Model |
| title_sort | predicting the compressive strength of concrete using an rbf ann model |
| topic | radial basis functions artificial neural networks prediction model compressive strength of concrete mix proportioning of concrete |
| url | https://www.mdpi.com/2076-3417/11/14/6382 |
| work_keys_str_mv | AT nanjingwu predictingthecompressivestrengthofconcreteusinganrbfannmodel |