Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/43117
標題: A combined thermographic analysis-Neural network methodology for eroded caves in a seawall
作者: Lee, T.L.
蔡清標
Tsai, C.P.
Lin, H.M.
Fang, C.J.
關鍵字: Eroded cave;Seawall;Thermography;Artificial neural network;infrared thermography;storm-surge;predictions;harbor
Project: Ocean Engineering
期刊/報告no:: Ocean Engineering, Volume 36, Issue 15-16, Page(s) 1251-1257.
摘要: 
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. (C) 2009 Elsevier Ltd. All rights reserved.
URI: http://hdl.handle.net/11455/43117
ISSN: 0029-8018
DOI: 10.1016/j.oceaneng.2009.07.009
Appears in Collections:土木工程學系所

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