Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks
This research was conducted to design an artificial neural network for predicting the compressive strength of self compacting concrete containing mineral admixtures. This prediction is divided into feed forward back propagation and reverse neural network model. The first part the model can predict t...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 语言: | English |
| 出版: |
2011
|
| 主题: | |
| 在线阅读: | http://eprints.usm.my/41368/1/ALI_PAPZAN.pdf |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| id |
my-usm-ep.41368 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-usm-ep.413682019-04-12T05:26:37Z Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks 2011-05 Papzan, Ali TA1-2040 Engineering (General). Civil engineering (General) This research was conducted to design an artificial neural network for predicting the compressive strength of self compacting concrete containing mineral admixtures. This prediction is divided into feed forward back propagation and reverse neural network model. The first part the model can predict the SCC compressive strength not only on experimental data but also on the every desired mineral admixture mix proportions. The network is able to pass the following way reversely. In other words, the network is acting as two-way routes. The first is the way which the starting point is amount of mineral admixtures (as input data) and the end point is the SCC compressive strength at 28 and 90 day (as desired output), the return way is vice versa. 2011-05 Thesis http://eprints.usm.my/41368/ http://eprints.usm.my/41368/1/ALI_PAPZAN.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuteraan Awam |
| institution |
Universiti Sains Malaysia |
| collection |
USM Institutional Repository |
| language |
English |
| topic |
TA1-2040 Engineering (General) Civil engineering (General) |
| spellingShingle |
TA1-2040 Engineering (General) Civil engineering (General) Papzan, Ali Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks |
| description |
This research was conducted to design an artificial neural network for predicting the compressive strength of self compacting concrete containing mineral admixtures. This prediction is divided into feed forward back propagation and reverse neural network model. The first part the model can predict the SCC compressive strength not only on experimental data but also on the every desired mineral admixture mix proportions. The network is able to pass the following way reversely. In other words, the network is acting as two-way routes. The first is the way which the starting point is amount of mineral admixtures (as input data) and the end point is the SCC compressive strength at 28 and 90 day (as desired output), the return way is vice versa. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Papzan, Ali |
| author_facet |
Papzan, Ali |
| author_sort |
Papzan, Ali |
| title |
Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks |
| title_short |
Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks |
| title_full |
Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks |
| title_fullStr |
Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks |
| title_full_unstemmed |
Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks |
| title_sort |
forecasting the compressive strength of self-compacting concretes containing mineral admixtures by artificial neural networks |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Kejuteraan Awam |
| publishDate |
2011 |
| url |
http://eprints.usm.my/41368/1/ALI_PAPZAN.pdf |
| _version_ |
1747820919159521280 |