Kibum Kim, Heechang Kang, Taehyeon Kim, D. T. Iseley, Jaeho Choi, J. Koo
{"title":"Influencing factors analysis for drinking water steel pipe pitting corrosion using artificial neural network","authors":"Kibum Kim, Heechang Kang, Taehyeon Kim, D. T. Iseley, Jaeho Choi, J. Koo","doi":"10.1080/1573062X.2023.2198996","DOIUrl":null,"url":null,"abstract":"ABSTRACT Steel is a metal, and thus, it undergoes corrosion over time. The comprehensive analysis of the factors influencing corrosion can aid in developing strategies, such as new ways to avoid corrosive environments. This study explored the factors influencing pitting corrosion in steel water pipes in South Korea between 1988–2020, using artificial neural networks. Partial dependence plots and variable importance are used to identify the degree of influence of the 12 corrosion-influencing factors. Pipe age had the highest importance and strongest influence on corrosion among the corrosion-influencing factors. Soil resistivity strongly influenced external corrosion, especially at values less than 5,000 Ω-cm, and the influence of sulfide concentration on external corrosion was also relatively strong. Water alkalinity exhibited the strongest influence on internal corrosion. This study will serve as reference data for developing corrosion depth prediction models and will contribute to understanding corrosive environments when laying new pipelines and improving existing ones.","PeriodicalId":49392,"journal":{"name":"Urban Water Journal","volume":"20 1","pages":"550 - 563"},"PeriodicalIF":1.6000,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Urban Water Journal","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1080/1573062X.2023.2198996","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT Steel is a metal, and thus, it undergoes corrosion over time. The comprehensive analysis of the factors influencing corrosion can aid in developing strategies, such as new ways to avoid corrosive environments. This study explored the factors influencing pitting corrosion in steel water pipes in South Korea between 1988–2020, using artificial neural networks. Partial dependence plots and variable importance are used to identify the degree of influence of the 12 corrosion-influencing factors. Pipe age had the highest importance and strongest influence on corrosion among the corrosion-influencing factors. Soil resistivity strongly influenced external corrosion, especially at values less than 5,000 Ω-cm, and the influence of sulfide concentration on external corrosion was also relatively strong. Water alkalinity exhibited the strongest influence on internal corrosion. This study will serve as reference data for developing corrosion depth prediction models and will contribute to understanding corrosive environments when laying new pipelines and improving existing ones.
期刊介绍:
Urban Water Journal provides a forum for the research and professional communities dealing with water systems in the urban environment, directly contributing to the furtherance of sustainable development. Particular emphasis is placed on the analysis of interrelationships and interactions between the individual water systems, urban water bodies and the wider environment. The Journal encourages the adoption of an integrated approach, and system''s thinking to solve the numerous problems associated with sustainable urban water management.
Urban Water Journal focuses on the water-related infrastructure in the city: namely potable water supply, treatment and distribution; wastewater collection, treatment and management, and environmental return; storm drainage and urban flood management. Specific topics of interest include:
network design, optimisation, management, operation and rehabilitation;
novel treatment processes for water and wastewater, resource recovery, treatment plant design and optimisation as well as treatment plants as part of the integrated urban water system;
demand management and water efficiency, water recycling and source control;
stormwater management, urban flood risk quantification and management;
monitoring, utilisation and management of urban water bodies including groundwater;
water-sensitive planning and design (including analysis of interactions of the urban water cycle with city planning and green infrastructure);
resilience of the urban water system, long term scenarios to manage uncertainty, system stress testing;
data needs, smart metering and sensors, advanced data analytics for knowledge discovery, quantification and management of uncertainty, smart technologies for urban water systems;
decision-support and informatic tools;...