Intelligent Approaches for Predicting Failure of Water Mains-Reference-Cited by-同舟云学术

Intelligent Approaches for Predicting Failure of Water Mains

Published:2020-11 Issue:4 Volume:11 Page:04020044
ISSN:1949-1190
Container-title:Journal of Pipeline Systems Engineering and Practice
language:en
Short-container-title:J. Pipeline Syst. Eng. Pract.

Author:

Almheiri Zainab¹,Meguid Mohamed²,Zayed Tarek³

Affiliation:

1. Ph.D. Candidate, Dept. of Civil Engineering and Applied Mechanics, McGill Univ., Montreal, QC, Canada H2X 3P7 (corresponding author).

2. Professor and Chair, Dept. of Civil Engineering and Applied Mechanics, McGill Univ., Montreal, QC, Canada H2X 3P7.

3. Professor, Faculty of Construction and Environment, Dept. of Building and Real Estate, Hong Kong Polytechnic Univ., Kowloon, Hong Kong.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29PS.1949-1204.0000485

Reference65 articles.

1. Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods

2. ASCE. 2017. “2017 infrastructure report card.” Accessed July 10 2019. https://www.infrastructurereportcard.org/.

3. Artificial neural networks: fundamentals, computing, design, and application

4. Botchkarev A. 2018. “Performance metrics (error measures) in machine learning regression forecasting and prognostics: Properties and typology.” Accessed June 16 2019. https://arxiv.org/abs/1809.03006.

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