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A combined thermographic analysis—Neural network methodology for eroded caves in a seawall
- Source :
-
Ocean Engineering . Nov2009, Vol. 36 Issue 15/16, p1251-1257. 7p. - Publication Year :
- 2009
-
Abstract
- Abstract: An application of an artificial neural network (ANN) combined with thermographic analysis for estimating the depth of eroded caves in a seawall is presented in this paper. A model experiment was first conducted in a sandbox using a thermographic device to detect the interior conditions of a structure from its temperature changes measured on the surface. The temperature difference calculated from the air temperature and the measured concrete surface point on a thermographic image was obtained for the neural network. Based on the laboratory data, an optimum ANN model for the estimation of the depth of eroded caves in a seawall was established by using four input factors: the site temperature, humidity, thermographic area, and the temperature difference. The model was verified using data from a seawall in Tainan City, Taiwan. From the results, it was found that the present ANN model efficiently estimates the depth of eroded caves in a seawall. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00298018
- Volume :
- 36
- Issue :
- 15/16
- Database :
- Academic Search Index
- Journal :
- Ocean Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 44585936
- Full Text :
- https://doi.org/10.1016/j.oceaneng.2009.07.009