Abstract
Concrete carbonation is one of the major causes of steel corrosion in reinforced concrete structure that can lead to shortened service life. Several carbonation prediction models including mathematical and neural network are available. The mathematical models are simplified and do not take all influential parameters of concrete materials into consideration. Most of the existing neural network based carbonation prediction models do not employ all parameters that influence the microstructural properties of the concrete. They also failed to perform certain essential steps during the model development, which in turn degrade their performance. In this work, novel neural network based carbonation prediction model is proposed. The model selects most relevant parameters, and removes irrelevant and/or redundant features from the original input data to build robust learning models. The performance evaluation of the model shows that the proposed carbonation prediction model predicts reasonably well with increased generalization ability.
Original language | English |
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Title of host publication | International Symposium Non-Destructive Testing in Civil Engineering (NDT-CE) September 15 - 17, 2015, Berlin, Germany |
Pages | 1074-1083 |
Number of pages | 10 |
Publication status | Published - 2015 |
MoE publication type | A4 Article in a conference publication |
Event | International Symposium Non-Destructive Testing in Civil Engineering, NDTCE 2015 - Berlin, Germany Duration: 15 Sept 2015 → 17 Sept 2015 https://www.ndt.net/search/docs.php3?MainSource=178&sessionID=1200 (International Symposium Non-Destructive Testing in Civil Engineering (NDTCE 2015), 15-17 Sep 2015, Berlin, Germany) |
Publication series
Series | NDT.net |
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Conference
Conference | International Symposium Non-Destructive Testing in Civil Engineering, NDTCE 2015 |
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Abbreviated title | NDTCE 2015 |
Country/Territory | Germany |
City | Berlin |
Period | 15/09/15 → 17/09/15 |
Internet address |
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Keywords
- concrete carbonation
- carbonation prediction
- modelling
- neural network