Deciphering and predict corrosion effect, influencing factors and microbial mechanism of sewer concrete corrosion based on extensive data analysis and machine learning-Reference-Cited by-同舟云学术

Deciphering and predict corrosion effect, influencing factors and microbial mechanism of sewer concrete corrosion based on extensive data analysis and machine learning

Published:2023-09-06 Issue:9 Volume:20 Page:1219-1230
ISSN:1573-062X
Container-title:Urban Water Journal
language:en
Short-container-title:Urban Water Journal

Author:

Wang Wenhao¹,Xu Xinxin²,Cao Jingguo¹,Zeng Ming³,Zhang Wu³

Affiliation:

1. College of Chemical Engineering and Material Science, Tianjin University of Science & Technology, Tianjin, China

2. Department of Environmental Engineering, College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, China

3. College of Marine and Environmental Sciences, Tianjin University of Science & Technology, Tianjin, China

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/1573062X.2023.2254739

Reference52 articles.

1. Reliability Assessment of Reinforced Concrete Sewer Pipes under Adverse Environmental Conditions: Case Study for the City of Arlington, Texas

2. Acidic Corrosion of Hydrated Cement Based Materials;Allahverdi A.;Ceram. Silikáty,2000

3. Bacterial Kinetics of Sulfur Oxidizing Bacteria and Their Biodeterioration Rates of Concrete Sewer Pipe Samples

4. High-Throughput Amplicon Sequencing Reveals Distinct Communities within a Corroding Concrete Sewer System

5. New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water

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1. Predicting Microbiologically Influenced Concrete Corrosion in Self-Cleansing Sewers Using Meta-Learning Techniques;Corrosion;2024-01-16