التفاصيل البيبلوغرافية
| العنوان: |
Prediction of compressive strength of geopolymer composites using an artificial neural network. |
| المؤلفون: |
Yadollahi, M. M., Benli, A., Demirboğa, R. |
| المصدر: |
Energy Materials; Sep2015, Vol. 10 Issue 3, p453-458, 6p |
| مصطلحات موضوعية: |
COMPRESSIVE strength, POLYMERIC composites, PUMICE, RADIO wave propagation, ARTIFICIAL neural networks |
| مستخلص: |
Geopolymers are highly complex materials which involve many variables and make for which modelling the properties is very difficult. There is no systematic approach in mix design for geopolymers. Since the amounts of silicamodulus, Na2O content, w/b ratios and curing time have a great influence on the compressive strength, an ANN (artificial neural network) method has been established for predicting compressive strength of ground pumice based Geopolymers and the possibilities of adapting ANN and artificial intelligence system for predicting the compressive strength have been studied. Consequently, a multilayer ANN by using back propagation architecture can be developed for geopolymer compressive strength prediction. In this study, the coefficient of determination (R²) has been used for investigating the proposedmodel accuracy. As a result, proposed ANNmodel can predict the compressive strength of geopolymer with R²=0.958. [ABSTRACT FROM AUTHOR] |
|
Copyright of Energy Materials is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Supplemental Index |
