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.
Original language | English |
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Pages (from-to) | 1251-1257 |
Number of pages | 7 |
Journal | Ocean Engineering |
Volume | 36 |
Issue number | 15-16 |
DOIs | |
Publication status | Published - 2009 Nov 1 |
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Ocean Engineering