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 |
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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 |
| Summary: | <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> |
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| ISSN: | 1813-162X 2312-7589 |