A combined thermographic analysis-Neural network methodology for eroded caves in a seawall

Tsung Lin Lee, Ching Piao Tsai, Hung-Ming Lin, Chi Jen Fang

Research output: Contribution to journalArticle

7 Citations (Scopus)

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 languageEnglish
Pages (from-to)1251-1257
Number of pages7
JournalOcean Engineering
Volume36
Issue number15-16
DOIs
Publication statusPublished - 2009 Nov 1

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Caves
Retaining walls
Neural networks
Temperature
Atmospheric humidity
Concretes
Air
Experiments

All Science Journal Classification (ASJC) codes

  • Ocean Engineering
  • Environmental Engineering

Cite this

Lee, Tsung Lin ; Tsai, Ching Piao ; Lin, Hung-Ming ; Fang, Chi Jen. / A combined thermographic analysis-Neural network methodology for eroded caves in a seawall. In: Ocean Engineering. 2009 ; Vol. 36, No. 15-16. pp. 1251-1257.
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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 journalArticle

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