| Autor: |
Ongpeng Jason Maximino, Clemente Spiro Jensen, Ong Carl Gavin, Te Dustin Dominic, Tecson Jerson Vincent, Roxas Cheryl Lyne |
| Jazyk: |
English<br />French |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
E3S Web of Conferences, Vol 347, p 02004 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2267-1242 |
| DOI: |
10.1051/e3sconf/202234702004 |
| Popis: |
Supplementary cementitious materials have been proven to be effective partial cement replacements in concrete to reduce greenhouse gas emissions from the use of ordinary Portland cement. In this study, artificial neural network was used to arrive at a predictive model to assess their effects in the compressive strength of concrete. Collection of 991 datasets from published literatures was done for the development of the best network model with acceptable root mean square error for both training and testing datasets. The supplementary cementitious materials were ranked accordingly using the improved stepwise method and network simulation. From the results, ground granulated blast-furnace slag with 15% cement replacement and silica fume with 30% cement replacement contributed to the highest increase in compressive strength. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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