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...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2011
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| Online Access: | http://eprints.usm.my/41368/ http://eprints.usm.my/41368/1/ALI_PAPZAN.pdf |
| _version_ | 1848879271734411264 |
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| author | Papzan, Ali |
| author_facet | Papzan, Ali |
| author_sort | Papzan, Ali |
| building | USM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T17:44:35Z |
| format | Thesis |
| id | usm-41368 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T17:44:35Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-413682019-04-12T05:26:37Z http://eprints.usm.my/41368/ Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks 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 NonPeerReviewed application/pdf en http://eprints.usm.my/41368/1/ALI_PAPZAN.pdf Papzan, Ali (2011) Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks. Masters thesis, Universiti Sains Malaysia. |
| 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 |
| title | 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_short | 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 |
| topic | TA1-2040 Engineering (General). Civil engineering (General) |
| url | http://eprints.usm.my/41368/ http://eprints.usm.my/41368/1/ALI_PAPZAN.pdf |