Artificial Neural Network Model for Predicting Compressive
<p> Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28<sup>th</sup> day after concrete placement. Therefore, strength estimation of co...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Tikrit University
2013-05-01
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| Series: | Tikrit Journal of Engineering Sciences |
| Subjects: | |
| Online Access: | http://www.tj-es.com/ojs/index.php/tjes/article/view/228 |
| _version_ | 1827846551161012224 |
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| author | Salim T. Yousif Salwa M. Abdullah |
| author_facet | Salim T. Yousif Salwa M. Abdullah |
| author_sort | Salim T. Yousif |
| collection | DOAJ |
| description | <p> Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28<sup>th</sup> day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS), and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.</p><p> The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c) is the most significant factor affecting the output of the model.</p><p> The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.</p> |
| first_indexed | 2024-03-12T09:17:36Z |
| format | Article |
| id | doaj.art-07c0ca476ab14768970ff565a0620aa2 |
| institution | Directory Open Access Journal |
| issn | 1813-162X 2312-7589 |
| language | English |
| last_indexed | 2024-03-12T09:17:36Z |
| publishDate | 2013-05-01 |
| publisher | Tikrit University |
| record_format | Article |
| series | Tikrit Journal of Engineering Sciences |
| spelling | doaj.art-07c0ca476ab14768970ff565a0620aa22023-09-02T14:42:31ZengTikrit UniversityTikrit Journal of Engineering Sciences1813-162X2312-75892013-05-011635566167Artificial Neural Network Model for Predicting CompressiveSalim T. YousifSalwa M. Abdullah<p> Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28<sup>th</sup> day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS), and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.</p><p> The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c) is the most significant factor affecting the output of the model.</p><p> The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.</p>http://www.tj-es.com/ojs/index.php/tjes/article/view/228Artificial neural network, Compressive strength, Concrete, Mixing, Predicting |
| spellingShingle | Salim T. Yousif Salwa M. Abdullah Artificial Neural Network Model for Predicting Compressive Tikrit Journal of Engineering Sciences Artificial neural network, Compressive strength, Concrete, Mixing, Predicting |
| title | Artificial Neural Network Model for Predicting Compressive |
| title_full | Artificial Neural Network Model for Predicting Compressive |
| title_fullStr | Artificial Neural Network Model for Predicting Compressive |
| title_full_unstemmed | Artificial Neural Network Model for Predicting Compressive |
| title_short | Artificial Neural Network Model for Predicting Compressive |
| title_sort | artificial neural network model for predicting compressive |
| topic | Artificial neural network, Compressive strength, Concrete, Mixing, Predicting |
| url | http://www.tj-es.com/ojs/index.php/tjes/article/view/228 |
| work_keys_str_mv | AT salimtyousif artificialneuralnetworkmodelforpredictingcompressive AT salwamabdullah artificialneuralnetworkmodelforpredictingcompressive |