Using Artificial Intelligence for Water Pipeline Infrastructure Asset Management-Reference-Cited by-同舟云学术

Using Artificial Intelligence for Water Pipeline Infrastructure Asset Management

Published:2022-07-28 Issue: Volume: Page:
ISSN:
Container-title:Pipelines 2022
language:
Short-container-title:

Author:

Vekaria Darshan¹²²,Vishwakarma Anmol¹²²,Sinha Sunil¹²²

Affiliation:

1. Dept. of Computer Science, Virginia Tech.

2. Dept. of Civil and Environmental Engineering, Virginia Tech.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784484302.002

Reference16 articles.

1. Folkman S. (2018). “Water Main Break Rates In the USA and Canada: A Comprehensive Study”. Mechanical and Aerospace Engineering Faculty Publications. Paper 174. https://digitalcommons.usu.edu/mae_facpub/174.

2. Artificial intelligence for the modeling of water pipes deterioration mechanisms;Dawood T.;Automation in Construction,2020

3. Fact Sheet: The Bipartisan Infrastructure Deal. (2021). “The White House”. https://www.whitehouse.gov/briefing-room/statements-releases/2021/11/06/fact-sheet-the-bipartisan-infrastructure-deal/.

4. Local Government Investment in Water and Sewer 2000-2015. “The United States Conference of Mayors”. https://www.usmayors.org/2018/01/10/local-government-investment-in-water-and-sewer-2000-2015/.

5. Baird G. et al. (2019). “How Cost Effective Is Machine Learning/AI Applied to Leak Detection and Pipe Replacement Prioritization” Pipelines 2019 ASCE.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Privacy-preserving culvert predictive models: A federated learning approach;Advanced Engineering Informatics;2024-08

2. An Improved Hybrid XGBoost Model for Culvert Inspection Using Swarm Intelligence Algorithms;Computing in Civil Engineering 2023;2024-01-25