Implementation of Machine Learning Techniques for Prediction of the Corrosion Depth for Water Pipelines-Reference-Cited by-同舟云学术

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Implementation of Machine Learning Techniques for Prediction of the Corrosion Depth for Water Pipelines

Published:2023-08-10 Issue: Volume: Page:
ISSN:
Container-title:Pipelines 2023
language:
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Author:

Kim Taehyeon¹²³⁴⁵,Kim Kibum¹²³⁴⁵,Kim Jinwon¹²³⁴⁵,Kim Jinkeun¹²³⁴⁵,Koo Jayong¹²³⁴⁵

Affiliation:

1. Research Professor, Dept. of Environmental Engineering, Univ. of Seoul, Seoul, Republic of Korea.

2. Visiting Scholar, Division of Construction Engineering and Management, Purdue Univ., West Lafayette, IN.

3. President, Water Resource Engineering Corporation, Daejeon, Republic of Korea.

4. Professor, Dept. of Environmental Engineering, Jeju Nation Univ., Jeju-do, Republic of Korea.

5. Professor, Dept. of Environmental Engineering, Univ. of Seoul, Seoul, Republic of Korea.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485033.016

Reference23 articles.

1. Highlights of Key International Water Infrastructure Asset Management Initiatives, and Trends, Challenges and Developments in Portugal;Amaral R.;Water Policy,2017

2. Gay, L. F., and Sinha, S. K. (2014). Water infrastructure asset management primer. London, UK: IWA Publishing.

3. Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1–758). New York: Springer.

4. Probabilistic physical modelling of corroded cast iron pipes for lifetime prediction

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