Computational Intelligence for Urban Infrastructure Condition Assessment: Water Transmission and Distribution Systems

Water transmission and distribution systems are critical urban infrastructure. The aging of water mains can lead to increased breakage rate, decreased hydraulic capacity, and deterioration of water quality. Condition assessment of water mains encompasses building computational model of failures, discerning distress indicators from inspection, rating health condition, and forecasting future failures. In this process, computational intelligence helps to achieve high-level awareness of system condition and facilitates the decision making in water main renewal and rehabilitation using the combined information from field knowledge, historical records, inspection results, and sensory data. This paper reviews computational approaches to achieve condition assessment of water mains. Inspection and sensor technologies involved in the assessment process are also briefly discussed.

[1]  Balvant Rajani,et al.  Comprehensive review of structural deterioration of water mains: statistical models , 2001 .

[2]  James A. Liggett,et al.  LEAKS IN PIPE NETWORKS , 1992 .

[3]  Andrew J. Day,et al.  Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system , 2003, Inf. Fusion.

[4]  Charles R. Farrar,et al.  Structural Health Monitoring: A Machine Learning Perspective , 2012 .

[5]  K Sridharan,et al.  Inverse transient analysis in pipe networks , 1996 .

[6]  Sa Sinske,et al.  A spatial decision support system for pipe-break susceptibility analysis of municipal water distribution systems , 2004 .

[7]  E. Benjamin Wylie,et al.  The Microcomputer and Pipeline Transients , 1983 .

[8]  M. Beller,et al.  Combined In-Line Inspection of Pipelines for Metal Loss and Cracks , 2006 .

[9]  Dalius Misiunas,et al.  Failure Monitoring and Asset Condition Assessment in Water Supply Systems , 2005 .

[10]  Chyr Pyng Liou,et al.  Pipeline Leak Detection by Impulse Response Extraction , 1998 .

[11]  Sehwan Kim,et al.  Remote structural health monitoring systems for next generation SCADA , 2013 .

[12]  Michael L. Cox,et al.  Industry working to protect the environment , 2001 .

[13]  Younho Cho,et al.  Recent Advances on Guided Waves in Pipe Inspection , 2008 .

[14]  Zheng Liu,et al.  Survey: State of the Art in NDE Data Fusion Techniques , 2007, IEEE Transactions on Instrumentation and Measurement.

[15]  Samuel T. Ariaratnam,et al.  Development of a Free-Swimming Acoustic Tool for Liquid Pipeline Leak Detection Including Evaluation for Natural Gas Pipeline Applications , 2010 .

[16]  Robi Polikar,et al.  A DATA FUSION SYSTEM FOR THE NONDESTRUCTIVE EVALUATION OF NON-PIGGABLE PIPES , 2005 .

[17]  H. Kwun,et al.  The magnetostrictive sensor technology for long range guided wave testing and monitoring of structures , 2003 .

[18]  D. Levesque,et al.  Flexible ultrasonic transducers , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[19]  L. I. Lagutskaya,et al.  Computer aided tomography , 1990 .

[20]  Gui Yun Tian,et al.  New Techniques for the Quantification of Defects Through Pulsed Magnetic Flux Leakage , 2008 .

[21]  Jiao Yang,et al.  Leak Identification Method for Buried Gas Pipeline Based on Spatial-Temporal Data Fusion , 2007, 2007 IEEE International Conference on Control and Automation.

[22]  Guangzhi Wang,et al.  Leak detection for transport pipelines based on autoregressive modeling , 1993 .

[23]  Vladan Babovic,et al.  A Data Mining Approach to Modelling of Water Supply Assets , 2002 .

[24]  Graham Bell,et al.  Development And Application Of Ductile Iron Pipe Electrical Resistance Probes For Monitoring Underground External Pipeline Corrosion , 2007 .

[25]  C. P. Liou.  Mass Imbalance Error of Waterhammer Equations and Leak Detection , 1994 .

[26]  Jean F. Bussière,et al.  High-temperature integrated and flexible ultrasonic transducers for nondestructive testing , 2009 .

[27]  Godfrey A. Walters,et al.  Hydroinformatics, data mining and maintenance of UK water networks , 1999 .

[28]  Osama Moselhi,et al.  Discussion of Prediction of Water Pipe Asset Life Using Neural Networks by D. Achim et al , 2008 .

[29]  Graeme C. Dandy,et al.  Optimal Scheduling of Water Pipe Replacement Using Genetic Algorithms , 2001 .

[30]  Angus R. Simpson,et al.  LEAK DETECTION IN PIPELINES USING THE DAMPING OF FLUID TRANSIENTS , 2002 .

[31]  Don Morison,et al.  Fiber Optic Sensors for Monitoring Pipe Bending Due to Ground Movement , 2006 .

[32]  Kerry J. McManus,et al.  Prediction of Water Pipe Asset Life Using Neural Networks , 2007 .

[33]  O. Hunaidi,et al.  1 Acoustic Methods for Locating Leaks in Municipal Water Pipe Networks , 2004 .

[34]  D. Davis.  The Application of Expert System and Agent Technology to Water Mains Rehabilitation Decision Making , 2022 .

[35]  Homayoun Najjaran,et al.  Condition Assessment of Buried Pipes Using Hierarchical Evidential Reasoning Model , 2008 .

[36]  Yi Zhou,et al.  Development of a Fuzzy Based Pipe Condition Assessment Model Using PROMETHEE , 2009 .

[37]  Sandra Lucia da Cruz,et al.  Pressure wave behaviour and leak detection in pipelines , 1996 .

[38]  Bill Spickelmire.  Corrosion considerations for ductile iron pipe , 2002 .

[39]  Vasant Honavar,et al.  Learn++: an incremental learning algorithm for supervised neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[40]  P. Cawley,et al.  Long Range Guided Wave Inspection Usage – Current Commercial Capabilities and Research Directions , 2007 .

[41]  P. Cawley,et al.  Guided wave permanently installed pipeline monitoring system , 2012 .

[42]  Homayoun Najjaran,et al.  Exploring the Relationship between Soil Properties and Deterioration of Metallic Pipes Using Predictive Data Mining Methods , 2010, J. Comput. Civ. Eng..

[43]  John C. Matthews,et al.  Field Demonstration of Innovative Condition Assessment Technologies for Water Mains: Leak Detection and Location , 2012 .

[44]  Xianglin Chen,et al.  Flexible eddy current sensor array for proximity sensing , 2007 .

[45]  A. Y. Allidina,et al.  Leak detection and location in gas pipelines , 1988 .

[46]  Xavier Maldague,et al.  Theory and Practice of Infrared Technology for Nondestructive Testing , 2001 .

[47]  Rehan Sadiq,et al.  Modelling the deterioration of buried infrastructure as a fuzzy Markov process , 2006 .

[48]  Jun Zhang,et al.  Statistical Pipeline Leak Detection Techniques for All Operating Conditions , 2000 .

[49]  Zheng Liu,et al.  State of the art review of inspection technologies for condition assessment of water pipes , 2013 .

[50]  Nicole Metje,et al.  Underground asset location and condition assessment technologies , 2007 .

[51]  L D Olson,et al.  IMPACT ECHO TESTING OF IN-SITU PRECAST CONCRETE CYLINDER PIPE , 1998 .

[52]  Zheng Liu,et al.  State-of-the-Art Review of Technologies for Pipe Structural Health Monitoring , 2012, IEEE Sensors Journal.

[53]  Magnus Moglia,et al.  Decision support system for water pipeline renewal prioritisation , 2006, J. Inf. Technol. Constr..

[54]  M S Higgins,et al.  Fiber Optic Sensors for Acoustic Monitoring of PCCP , 2006 .

[55]  Shivprakash Iyer,et al.  Automated condition assessment of buried sewer pipes based on digital imaging techniques , 2013 .

[56]  Zheng Liu,et al.  Classification of defects with ensemble methods in the automated visual inspection of sewer pipes , 2015, Pattern Analysis and Applications.

[57]  Angus R. Simpson,et al.  Leak Detection and Calibration Using Transients and Genetic Algorithms , 2000 .

[58]  B Anderson,et al.  Backscatter Computed Tomography (BCT) Pilot Project for Culvert Integrity Analysis in the City of Toronto , 2012 .

[59]  Rehan Sadiq,et al.  Fuzzy-Based Method to Evaluate Soil Corrosivity for Prediction of Water Main Deterioration , 2004 .

[60]  Homayoun Najjaran,et al.  Fuzzy Expert System to Assess Corrosion of Cast/Ductile Iron Pipes from Backfill Properties , 2006, Comput. Aided Civ. Infrastructure Eng..

[61]  Bruno Brunone,et al.  Transient Test-Based Technique for Leak Detection in Outfall Pipes , 1999 .

[62]  D. Atherton,et al.  Inspection of prestressed concrete pressure pipe , 2000 .

[63]  B Mergelas,et al.  Leak Locating Method for Pre-Commissioned Transmission Pipelines: North American Case Studies , 2005 .

[64]  Osama Hunaidi,et al.  GROUND-PENETRATING RADAR FOR DETECTION OF LEAKS IN BURIED PLASTIC WATER DISTRIBUTION PIPES , 1998 .

[65]  Lawrence A. Klein,et al.  Sensor and Data Fusion Concepts and Applications , 1993 .

[66]  Alfred E. Crouch,et al.  DEFECT ASSESSMENT USING CONFORMABLE ARRAY DATA , 2003 .

[67]  Alfred Barbian,et al.  Combined Quantitative In-Line Inspection of Pipelines for Metal Loss and Cracks , 2006 .

[68]  Nezih Mrad,et al.  Data Fusion for the Diagnostics, Prognostics, and Health Management of Aircraft Systems , 2014 .

[69]  Jay N. Meegoda,et al.  A conceptual framework for intelligent monitoring systems , 2005, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[70]  Tarek Zayed,et al.  Condition Rating Model for Underground Infrastructure Sustainable Water Mains , 2006 .

[71]  Rehan Sadiq,et al.  Translation of pipe inspection results into condition ratings using the fuzzy synthetic evaluation technique , 2006 .

[72]  Joseph L. Rose,et al.  Guided wave thickness measurement with EMATs , 2003 .

[73]  David Shaw,et al.  Genetic Programming within Civil Engineering , 2004 .