Progress in Drainage Pipeline Condition Assessment and Deterioration Prediction Models

Xuming Zeng, Zinan Wang, Hao Wang (), Shengyan Zhu and Shaofeng Chen
Additional contact information
Xuming Zeng: Powerchina Huadong Engineering Corporation Limited, Building 35, D District, Fuzhou Software Park, Tongpan Road, Fuzhou 350108, China
Zinan Wang: Zijin School of Geology and Mining, Fuzhou University, No. 2, Wulongjiang North Avenue, Fuzhou 350108, China
Hao Wang: Zijin School of Geology and Mining, Fuzhou University, No. 2, Wulongjiang North Avenue, Fuzhou 350108, China
Shengyan Zhu: Powerchina Huadong Engineering Corporation Limited, Building 35, D District, Fuzhou Software Park, Tongpan Road, Fuzhou 350108, China
Shaofeng Chen: Powerchina Huadong Engineering Corporation Limited, Building 35, D District, Fuzhou Software Park, Tongpan Road, Fuzhou 350108, China

Sustainability, 2023, vol. 15, issue 4, 1-29

Abstract: The condition of drainage pipes greatly affects the urban environment and human health. However, it is difficult to carry out economical and efficient pipeline investigation and evaluation due to the location and structure of drainage pipes. Herein, the four most-commonly used drainage pipeline evaluation standards have been synthesized and analyzed to summarize the deterioration and breakage patterns of drainage pipes. The common pipe breakage patterns are also summarized by integrating the literature and engineering experience. To systematically describe the condition of drainage pipes, a system of influencing factors for the condition of pipes, including physical, environmental, and operational factors, has been established, and the mechanism of action of each influencing factor has been summarized. Physical, statistical, and AI models and their corresponding representative models have been categorized, and the research progress of current mainstream drainage-pipe deterioration and breakage prediction models are reviewed in terms of their principles and progress in their application.

Keywords: pipeline condition assessment; pipeline deterioration and breakage; influencing factors; artificial intelligence model; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/4/3849/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/4/3849/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:4:p:3849-:d:1074550

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().