Abstract
| Original language | English |
|---|---|
| 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 Sep 2015 → 17 Sep 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 |
|---|
Conference
| Conference | International Symposium Non-Destructive Testing in Civil Engineering, NDTCE 2015 |
|---|---|
| Abbreviated title | NDTCE 2015 |
| Country | Germany |
| City | Berlin |
| Period | 15/09/15 → 17/09/15 |
| Internet address |
|
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Keywords
- concrete carbonation
- carbonation prediction
- modelling
- neural network
Cite this
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Optimized neural network based carbonation prediction model. / Taffese, Woubishet Z.; Al-Neshawy, Fahim; Sistonen, Esko; Ferreira, Miguel.
International Symposium Non-Destructive Testing in Civil Engineering (NDT-CE) September 15 - 17, 2015, Berlin, Germany. 2015. p. 1074-1083 (NDT.net).Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Optimized neural network based carbonation prediction model
AU - Taffese, Woubishet Z.
AU - Al-Neshawy, Fahim
AU - Sistonen, Esko
AU - Ferreira, Miguel
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - concrete carbonation
KW - carbonation prediction
KW - modelling
KW - neural network
M3 - Conference article in proceedings
T3 - NDT.net
SP - 1074
EP - 1083
BT - International Symposium Non-Destructive Testing in Civil Engineering (NDT-CE) September 15 - 17, 2015, Berlin, Germany
ER -