Leveraging Machine Learning for Pipeline Condition Assessment-Reference-Cited by-同舟云学术

Leveraging Machine Learning for Pipeline Condition Assessment

Published:2023-08 Issue:3 Volume:14 Page:
ISSN:1949-1190
Container-title:Journal of Pipeline Systems Engineering and Practice
language:en
Short-container-title:J. Pipeline Syst. Eng. Pract.

Author:

Lu Hongfang¹^ORCID,Xu Zhao-Dong²,Zang Xulei³,Xi Dongmin⁴,Iseley Tom⁵,Matthews John C.⁶^ORCID,Wang Niannian⁷

Affiliation:

1. Associate Professor, China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast Univ., Nanjing 210096, China. ORCID: .

2. Professor, China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast Univ., Nanjing 210096, China (corresponding author).

3. Ph.D. Student, China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast Univ., Nanjing 210096, China.

4. Graduate Student, China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast Univ., Nanjing 210096, China.

5. Professor, Construction Engineering and Management, Purdue Univ., West Lafayette, IN 47907.

6. Professor and Director, Trenchless Technology Center, Louisiana Tech Univ., Ruston, LA 71270. ORCID: .

7. Professor, School of Water Science and Engineering, Zhengzhou Univ., Zhengzhou 450000.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JPSEA2.PSENG-1464

Reference133 articles.

1. Abbas A. K. R. Flori H. Almubarak J. Dawood H. Abbas and A. Alsaedi. 2019. “Intelligent prediction of stuck pipe remediation using machine learning algorithms.” In Proc. SPE Annual Technical Conf. and Exhibition. Calgary Canada: Society of Petroleum Engineers.

2. Artificial intelligence-based machine learning considering flow and temperature of the pipeline for leak early detection using acoustic emission

3. Alshaikh A. A. Magana-Mora S. A. Gharbi and A. Al-Yami. 2019. “Machine learning for detecting stuck pipe incidents: Data analytics and models evaluation.” In Proc. Int. Petroleum Technology Conf. Calgary Canada: Society of Petroleum Engineers.

4. Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data

5. Risk assessment for pipelines with active defects based on artificial intelligence methods

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