Water pipe failure prediction and risk models: state-of-the-art review

This review paper presents the current state-of-the-art pertains to water pipe failure prediction and risk assessment, published in the last ten years (2009–2019). This paper has been motivated by ...

[1]  Jon Røstum,et al.  Statistical modelling of pipe failures in water networks , 2000 .

[2]  Thomas M. Walski,et al.  Economic analysis of water main breaks , 1982 .

[3]  Daniele B. Laucelli,et al.  Development of rehabilitation plans for water mains replacement considering risk and cost-benefit assessment , 2006 .

[4]  Daniela Fuchs-Hanusch,et al.  Whole of Life Cost Calculations for Water Supply Pipes , 2011 .

[5]  Danial Jahed Armaghani,et al.  Application of artificial neural network for predicting shaft and tip resistances of concrete piles , 2015 .

[6]  Isam Shahrour,et al.  Application of Artificial Neural Networks (ANN) to model the failure of urban water mains , 2010, Math. Comput. Model..

[7]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[8]  Małgorzata Kutyłowska,et al.  Neural network approach for failure rate prediction , 2015 .

[9]  Bahram Gharabaghi,et al.  Extreme learning machine model for water network management , 2017, Neural Computing and Applications.

[10]  S. Park,et al.  Survival Analysis of Water Distribution Pipe Failure Data Using the Proportional Hazards Model , 2008 .

[11]  Bijaya K. Panigrahi,et al.  A comparative study of wavelet families for EEG signal classification , 2011, Neurocomputing.

[12]  Yves Filion,et al.  State-of-the-art review of water pipe failure prediction models and applicability to large-diameter mains , 2017 .

[13]  Helio Aisenberg Ferenhof,et al.  Desmistificando a revisão de literatura como base para redação científica: método SFF DEMYSTIFYING THE LITERATURE REVIEW AS BASIS FOR SCIENTIFIC WRITING: SSF METHOD , 2016 .

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

[15]  NishiyamaMichael,et al.  Review of statistical water main break prediction models , 2013 .

[16]  Uri Shamir,et al.  An Analytic Approach to Scheduling Pipe Replacement , 1979 .

[17]  Jean-Pierre Villeneuve,et al.  Modeling Water Pipe Breaks—Three Case Studies , 2003 .

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

[19]  Tarek Zayed,et al.  Computer Vision-Based Model for Moisture Marks Detection and Recognition in Subway Networks , 2018, J. Comput. Civ. Eng..

[20]  Balvant Rajani,et al.  Comprehensive review of structural deterioration of water mains: physically based models , 2001 .

[21]  Dragan Savic,et al.  Deterioration modelling of small-diameter water pipes under limited data availability , 2017 .

[22]  Dragan Savic,et al.  Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling , 2009 .

[23]  Solomon Tesfamariam,et al.  Predicting water main failures: A Bayesian model updating approach , 2016, Knowl. Based Syst..

[24]  Kourosh Behzadian,et al.  Pipe Failure Prediction in Water Distribution Systems Considering Static and Dynamic Factors , 2017 .

[25]  Balvant Rajani,et al.  Exploration of the relationship between water main breaks and temperature covariates , 2012 .

[26]  D. Savić,et al.  Advances in data-driven analyses and modelling using EPR-MOGA. , 2009 .

[27]  Daniele B. Laucelli,et al.  Study on relationships between climate-related covariates and pipe bursts using evolutionary-based modelling , 2014 .

[28]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[29]  T. Zayed,et al.  Construction productivity fuzzy knowledge base management system , 2018 .

[30]  Mahmut Firat,et al.  Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods , 2015, Water Resources Management.

[31]  Alison M. St. Clair,et al.  State-of-the-technology review on water pipe condition, deterioration and failure rate prediction models! , 2012 .

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

[33]  Tarek Hegazy,et al.  Neural networks as tools in construction , 1991 .

[34]  J. Klein,et al.  Survival Analysis: Techniques for Censored and Truncated Data , 1997 .

[35]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[36]  Rehan Sadiq,et al.  Bayesian model averaging for the prediction of water main failure for small to large Canadian municipalities , 2016 .

[37]  Stefano Alvisi,et al.  Comparative analysis of two probabilistic pipe breakage models applied to a real water distribution system , 2010 .

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

[39]  Jill K. Jesson,et al.  Doing Your Literature Review: Traditional and Systematic Techniques , 2011 .

[40]  Desheng Dash Wu,et al.  Computational simulation and risk analysis: An introduction of state of the art research , 2013, Math. Comput. Model..

[41]  Symeon E. Christodoulou,et al.  Proactive Risk-Based Integrity Assessment of Water Distribution Networks , 2010 .