A prioritization method for replacement of water mains using rank aggregation

Pipe breaks in municipal water distribution networks may cause serious damage economically and socially. Existing methods for replacement scheduling of pipes do not provide practical indicators for replacing an individual deteriorated pipe. This work formulates the selection problem as the decision of preference ordering or ranking and proposes a bipartite ranking-based approach. The suggested approach also considers loss from broken pipes in terms of the costs associated with broken water main and its repair. We use rank aggregation method to integrate multiple ranks into replacement order of water mains. The suggested framework prioritizes current pipe sections for replacement based on the aggregated ranks. Multiple ranks given by the reliability of water pipe sections are aggregated and a cost effective policy for pipe replacement is derived.

[1]  Koen Jacques Ferdinand Blanckaert,et al.  Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network , 2013 .

[2]  Eyke Hllermeier,et al.  Preference Learning , 2010 .

[3]  Regina C. Elandt-Johnson,et al.  Survival Models and Data Analysis: Elandt-Johnson/Survival , 1999 .

[4]  E. Roshani,et al.  Event-Based Approach to Optimize the Timing of Water Main Rehabilitation with Asset Management Strategies , 2014 .

[5]  G. V. Loganathan,et al.  Methodology for economically optimal replacement of pipes in water distribution systems: 1. Theory , 2002 .

[6]  Yves Le Gat,et al.  Using maintenance records to forecast failures in water networks , 2000 .

[7]  N. Risch.  Searching for genetic determinants in the new millennium , 2000, Nature.

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

[9]  Costas S. Iliopoulos,et al.  An algorithm for mapping short reads to a dynamically changing genomic sequence , 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[10]  Ujjwal Maulik,et al.  Weighted Markov Chain Based Aggregation of Biomolecule Orderings , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[11]  N. L. Johnson,et al.  Survival Models and Data Analysis , 1982 .

[12]  Pascal Le Gauffre,et al.  A Multicriteria Decision Support Methodology for Annual Rehabilitation Programs of Water Networks , 2007, Comput. Aided Civ. Infrastructure Eng..

[13]  Neil S. Grigg.  Water Main Breaks: Risk Assessment and Investment Strategies , 2013 .

[14]  Avi Ostfeld,et al.  State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management , 2010 .

[15]  Hwandon Jun,et al.  Modeling of Water Main Failure Rates Using the Log-linear ROCOF and the Power Law Process , 2008 .

[16]  Jong Woo Kim,et al.  Dynamic optimization of maintenance and improvement planning for water main system: Periodic replacement approach , 2015, Korean Journal of Chemical Engineering.

[17]  Thomas Gärtner,et al.  Label Ranking Algorithms: A Survey , 2010, Preference Learning.

[18]  Thomas B. Moeslund,et al.  Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Andrzej P. Wierzbicki,et al.  The Need for and Possible Methods of Objective Ranking , 2010, Trends in Multiple Criteria Decision Analysis.

[20]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[21]  Nagen Nagarur,et al.  Optimal Maintenance Policy and Fund Allocation in Water Distribution Networks , 2005 .

[22]  Seth D. Guikema,et al.  Statistical models for the analysis of water distribution system pipe break data , 2009, Reliab. Eng. Syst. Saf..

[23]  Ari M Frank,et al.  A ranking-based scoring function for peptide-spectrum matches. , 2009, Journal of proteome research.

[24]  Marc Parizeau,et al.  Multiobjective Approach for Pipe Replacement Based on Bayesian Inference of Break Model Parameters , 2009 .

[25]  G. V. Loganathan,et al.  Threshold Break Rate for Pipeline Replacement in Water Distribution Systems , 2002 .

[26]  Seth D. Guikema,et al.  Bayesian Belief Networks for predicting drinking water distribution system pipe breaks , 2014, Reliab. Eng. Syst. Saf..

[27]  Graeme C. Dandy,et al.  Multi-Objective Trade-Offs between Cost and Reliability in the Replacement of Water Mains , 2006 .

[28]  Ana Debón,et al.  Comparing risk of failure models in water supply networks using ROC curves , 2010, Reliab. Eng. Syst. Saf..

[29]  Charles X. Ling,et al.  Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.

[30]  Tarek Zayed,et al.  Hierarchical Fuzzy Expert System for Risk of Failure of Water Mains , 2010 .