Development of PCCP wire breaks prediction model using artificial neural networks

Proceedings of Pipelines Congress 2008 - Pipeline Asset Management: Maximizing Performance of Our Pipeline Infrastructure

Volumn 321, Issue , 2008, Pages

  Amaitik, Nasser M ^a   Amaitik, Saleh M ^b  

^a Great Man Made River Authority   (Libyan Arab Jamahiriya)

^b UNIVERSITY OF BENGHAZI   (Libyan Arab Jamahiriya)

Author keywords

Acoustic Monitoring; ANN; GMRA; PCCP; Wire Breaks

Indexed keywords

ACOUSTIC MEASURING INSTRUMENTS; ASSET MANAGEMENT; CONCRETE PIPE; CONDITION MONITORING; DETERIORATION; FORECASTING; GEOPHYSICAL PROSPECTING; GROUND PENETRATING RADAR SYSTEMS; LINEAR REGRESSION; NEURAL NETWORKS; PRESTRESSED CONCRETE; SOILS; WIRE;

ACOUSTIC MONITORING; DETERIORATION PROCESS; GMRA; MULTIPLE LINEAR REGRESSION MODELS; PCCP; PERFORMANCE ASSESSMENT; PREDICTION PERFORMANCE; WIRE BREAKS;

PIPELINES;

EID: 70349392045     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1061/40994(321)128     Document Type: Conference Paper

References (6)

1. Achim, D., Ghotb, F. & McManus, K. 2007, 'Prediction of Water Pipe Asset Life Using Neural Networks', Journal of Infrastructure Systems, ASCE, pp. 26-30.
2. Al-Barqawi, H. & Zayed, T. 2006, 'Condition Rating Model for Underground Infrastructure Water Mains', Jou. of Constructed Facilities perf, ASCE, pp. 126-135.
3. Essamin, O. & Holley, M. 2004, 'Great Man Made River Authority (GMRA): The Role of Acoustic Monitoring in the Management of the Worlds Largest Prestressed Concrete Cylinder Pipe Project', Proceedings of ASCE, Aug., 1-4, San Diego, California.
4. Evaluation of CP Performance and Acoustic Monitoring results for White Pipe Sections on Phase I System - Technical Report, 2006, Planning, Follow-up and QA Dept., The Great Man-made River Authority, November 20, Benghazi, Libya.
5. Great Man-made River Authority (GMRA) Website, 2007 [Online], Available at: http://www.gmmra.org/ [Accessed during 10-19 July 2007].
6. Najafi, M. & Kulandaivel G. 2005, 'Pipeline Condition Prediction Using Neural Networks Models', Proceedings of ASCE, Aug., 21-24, Houston, Texas, pp. 767-781.