A combined thermography - Neural network for the prediction of eroded caves behind seawall

T. L. Lee; H. M. Lin; H. H. Chen; R. Y. Yang

A combined thermography - Neural network for the prediction of eroded caves behind seawall

T. L. Lee, H. M. Lin, H. H. Chen, R. Y. Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, the depth forecasting of eroded caves behind seawall model is proposed by using the back-propagation neural network combined the thermography analysis. The measured data of the depth of eroded caves behind seawall from the model experiments of sandbox and Cingcao seawall in Taiwan had been used to test the performance of the present model. From the results, it was found that the artificial neural network can efficiently forecast the depth of eroded caves behind a seawall using the four input factors, including the site temperature, humidity, thermograph area, and temperature difference

Original language	English
Title of host publication	The Proceedings of the 19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE
Pages	1299-1304
Number of pages	6
Publication status	Published - 2009
Event	19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE - Osaka, Japan Duration: 2009 Jun 21 → 2009 Jun 26

Publication series

Name	Proceedings of the International Offshore and Polar Engineering Conference
ISSN (Print)	1098-6189
ISSN (Electronic)	1555-1792

Other

Other	19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE
Country/Territory	Japan
City	Osaka
Period	09-06-21 → 09-06-26

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Ocean Engineering
Mechanical Engineering

Cite this

@inproceedings{50671c9613c240228b05f392f770ef22,

title = "A combined thermography - Neural network for the prediction of eroded caves behind seawall",

abstract = "In this paper, the depth forecasting of eroded caves behind seawall model is proposed by using the back-propagation neural network combined the thermography analysis. The measured data of the depth of eroded caves behind seawall from the model experiments of sandbox and Cingcao seawall in Taiwan had been used to test the performance of the present model. From the results, it was found that the artificial neural network can efficiently forecast the depth of eroded caves behind a seawall using the four input factors, including the site temperature, humidity, thermograph area, and temperature difference",

author = "Lee, {T. L.} and Lin, {H. M.} and Chen, {H. H.} and Yang, {R. Y.}",

year = "2009",

language = "English",

isbn = "9781880653531",

series = "Proceedings of the International Offshore and Polar Engineering Conference",

pages = "1299--1304",

booktitle = "The Proceedings of the 19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE",

note = "19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE ; Conference date: 21-06-2009 Through 26-06-2009",

}

Lee, TL, Lin, HM, Chen, HH & Yang, RY 2009, A combined thermography - Neural network for the prediction of eroded caves behind seawall. in The Proceedings of the 19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE. Proceedings of the International Offshore and Polar Engineering Conference, pp. 1299-1304, 19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE, Osaka, Japan, 09-06-21.

A combined thermography - Neural network for the prediction of eroded caves behind seawall. / Lee, T. L.; Lin, H. M.; Chen, H. H. et al.
The Proceedings of the 19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE. 2009. p. 1299-1304 (Proceedings of the International Offshore and Polar Engineering Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A combined thermography - Neural network for the prediction of eroded caves behind seawall

AU - Lee, T. L.

AU - Lin, H. M.

AU - Chen, H. H.

AU - Yang, R. Y.

PY - 2009

Y1 - 2009

N2 - In this paper, the depth forecasting of eroded caves behind seawall model is proposed by using the back-propagation neural network combined the thermography analysis. The measured data of the depth of eroded caves behind seawall from the model experiments of sandbox and Cingcao seawall in Taiwan had been used to test the performance of the present model. From the results, it was found that the artificial neural network can efficiently forecast the depth of eroded caves behind a seawall using the four input factors, including the site temperature, humidity, thermograph area, and temperature difference

AB - In this paper, the depth forecasting of eroded caves behind seawall model is proposed by using the back-propagation neural network combined the thermography analysis. The measured data of the depth of eroded caves behind seawall from the model experiments of sandbox and Cingcao seawall in Taiwan had been used to test the performance of the present model. From the results, it was found that the artificial neural network can efficiently forecast the depth of eroded caves behind a seawall using the four input factors, including the site temperature, humidity, thermograph area, and temperature difference

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M3 - Conference contribution

AN - SCOPUS:74549171120

SN - 9781880653531

T3 - Proceedings of the International Offshore and Polar Engineering Conference

SP - 1299

EP - 1304

BT - The Proceedings of the 19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE

T2 - 19th (2009) International OFFSHORE AND POLAR ENGINEERING CONFERENCE

Y2 - 21 June 2009 through 26 June 2009

ER -

A combined thermography - Neural network for the prediction of eroded caves behind seawall

Abstract

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All Science Journal Classification (ASJC) codes

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