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 |
|---|---|
| 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 |
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All Science Journal Classification (ASJC) codes
- Ocean Engineering
- Environmental Engineering
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A combined thermographic analysis-Neural network methodology for eroded caves in a seawall. / Lee, Tsung Lin; Tsai, Ching Piao; Lin, Hung-Ming; Fang, Chi Jen.
In: Ocean Engineering, Vol. 36, No. 15-16, 01.11.2009, p. 1251-1257.Research output: Contribution to journal › Article
TY - JOUR
T1 - A combined thermographic analysis-Neural network methodology for eroded caves in a seawall
AU - Lee, Tsung Lin
AU - Tsai, Ching Piao
AU - Lin, Hung-Ming
AU - Fang, Chi Jen
PY - 2009/11/1
Y1 - 2009/11/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=71749099348&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71749099348&partnerID=8YFLogxK
U2 - 10.1016/j.oceaneng.2009.07.009
DO - 10.1016/j.oceaneng.2009.07.009
M3 - Article
AN - SCOPUS:71749099348
VL - 36
SP - 1251
EP - 1257
JO - Ocean Engineering
JF - Ocean Engineering
SN - 0029-8018
IS - 15-16
ER -