Risk-Based Prioritization of Sewer Pipe Inspection from Infrastructure Asset Management Perspective

The escalating number of aging sewer pipes necessitates an infrastructure asset management approach to achieve an efficient budget allocation for maintenance. This study suggests a risk-based prioritization framework for sewer pipe inspection considering the predicted condition of sewer pipes and the criticality of the economic, social and environmental impacts associated with them. The results from both models can be used to evaluate the risk of sewer pipes by classification into risk groups. A risk matrix is used for the classification, and it divides the sewer pipes into five risk groups. The results of this study show an improvement in the accuracy of finding sewer pipes in a bad condition using this framework. The condition prediction model can successfully find sewer pipes with a bad condition with over 70% precision. High-risk sewer pipes are highlighted using the differences in the environmental features as well as in the physical features associated with other sewer pipes. Additionally, through the combination of both the condition and criticality of sewer pipes, the framework systemically prioritizes needed maintenance for sewer pipes with a very bad condition. This prioritization framework is expected to help the process of deciding which sewer pipes should be prioritized within a constrained budget.

[1]  Sharareh Kermanshachi,et al.  Factors Influencing the Condition of Sewer Pipes: State-of-the-Art Review , 2020 .

[2]  Ossama Salem,et al.  Risk Assessment of Wastewater Collection Lines Using Failure Models and Criticality Ratings , 2012 .

[3]  Amarjit Singh,et al.  Bathtub curves and pipe prioritization based on failure rate , 2013 .

[4]  Zekai Şen,et al.  Average Areal Precipitation by Percentage Weighted Polygon Method , 1998 .

[5]  N. Muttil,et al.  Impact of short duration intense rainfall events on sanitary sewer network performance , 2017 .

[6]  Ossama Salem,et al.  Modeling Failure of Wastewater Collection Lines Using Various Section-Level Regression Models , 2012 .

[7]  Dulcy M. Abraham,et al.  Optimization Modeling for Sewer Network Management , 1998 .

[8]  Tarek Zayed,et al.  Criticality Model to Prioritize Pipeline Rehabilitation Decisions , 2018 .

[9]  Choong-Ki Chung,et al.  Logistic regression model for sinkhole susceptibility due to damaged sewer pipes , 2018, Natural Hazards.

[10]  HarveyRobert Richard,et al.  Predicting the structural condition of individual sanitary sewer pipes with random forests , 2014 .

[11]  Dulcy M. Abraham,et al.  Estimating Transition Probabilities in Markov Chain-Based Deterioration Models for Management of Wastewater Systems , 2006 .

[12]  Matthew J. Xerri,et al.  Building a proactive engagement culture in asset management organizations , 2014 .

[13]  J. P. Davies,et al.  Factors influencing the structural deterioration and collapse of rigid sewer pipes , 2001 .

[14]  Y. Le Gat,et al.  Modelling the deterioration process of drainage pipelines , 2008 .

[15]  R Baur,et al.  Selective inspection planning with ageing forecast for sewer types. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[16]  Baris Salman,et al.  Infrastructure Management and Deterioration Risk Assessment of Wastewater Collection Systems , 2010 .

[17]  Solomon Tesfamariam,et al.  Consequence-based framework for buried infrastructure systems: A Bayesian belief network model , 2018, Reliab. Eng. Syst. Saf..

[18]  S. Smolders,et al.  An investigation of the factors influencing sewer structural deterioration , 2009 .

[19]  Z. Khan,et al.  Stochastic Analysis of Factors Affecting Sewer Network Operational Condition , 2009 .

[20]  N. Iulian,et al.  Study on the impact of sewer pipes on the environment , 2015 .

[21]  Richard N. Palmer,et al.  Expert System for Prioritizing the Inspection of Sewers: Knowledge Base Formulation and Evaluation , 2002 .

[22]  L. Corominas,et al.  Sediment Level Prediction of a Combined Sewer System Using Spatial Features , 2021, Sustainability.

[23]  Tarek Zayed,et al.  Condition Prediction for Chemical Grouting Rehabilitation of Sewer Networks , 2016 .

[24]  T. Saaty.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[25]  Massoud Tabesh,et al.  Risk assessment model to prioritize sewer pipes inspection in wastewater collection networks. , 2017, Journal of environmental management.

[26]  Brajesh Dubey,et al.  A risk-based approach to sanitary sewer pipe asset management. , 2015, The Science of the total environment.

[27]  John C. Matthews,et al.  Consequence-of-Failure Model for Risk-Based Asset Management of Wastewater Pipes Using AHP , 2019, Journal of Pipeline Systems Engineering and Practice.

[28]  Samuel T. Ariaratnam,et al.  Assessment of Infrastructure Inspection Needs Using Logistic Models , 2001 .

[29]  Myung Jin Chae.  Automated interpretation and assessment of sewer pipeline infrastructure , 2001 .

[30]  D. H Tran,et al.  Neural Network Based Prediction Models For StructuralDeterioration of Urban Drainage Pipes , 2007 .

[31]  Mohamed Elmasry,et al.  A state of the art review on condition assessment models developed for sewer pipelines , 2020, Eng. Appl. Artif. Intell..

[32]  Dulcy M. Abraham,et al.  An Ordered Probit Model Approach for Developing Markov Chain Based Deterioration Model for Wastewater Infrastructure Systems , 2005 .

[33]  Mohammadreza Malek mohammadi,et al.  DEVELOPMENT OF CONDITION PREDICTION MODELS FOR SANITARY SEWER PIPES , 2019 .

[34]  W. Bauwens,et al.  Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods , 2010 .

[35]  Helge Brattebø,et al.  Asset Management for Urban Wastewater Pipeline Networks , 2010 .

[36]  이재현,et al.  Sewer CCTV Inspection Prioritization Based on Risk Assessment , 2017 .

[37]  Tarek Zayed,et al.  Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines , 2008 .

[38]  Guru Kulandaivel,et al.  Pipeline Condition Prediction Using Neural Network Models , 2005 .

[39]  Ken Henderson,et al.  Integrated Asset Management – An Investment in Sustainability , 2014 .

[40]  Andrés Torres,et al.  Identifying explanatory variables of structural state for optimum asset management of urban drainage networks: a pilot study for the city of Bogota , 2017 .

[41]  Dulcy M. Abraham,et al.  CHALLENGING ISSUES IN MODELING DETERIORATION OF COMBINED SEWERS , 2001 .

[42]  Mahmoud R. Halfawy,et al.  Integrated Decision Support System for Optimal Renewal Planning of Sewer Networks , 2008 .

[43]  S. Wayne Miles,et al.  Setting Pipeline Rehabilitation Priorities to Achieve "Best" Results — A Case Study Using Condition and Criticality Criteria , 2007 .

[44]  Tarek Zayed,et al.  Simulation-based deterioration patterns of water pipelines , 2019, Structure and Infrastructure Engineering.

[45]  Rehan Sadiq,et al.  Modeling failure risk in buried pipes using fuzzy Markov deterioration process , 2019 .

[46]  Osama Moselhi,et al.  Decision Support Model for Integrated Risk Assessment and Prioritization of Intervention Plans of Municipal Infrastructure , 2016 .

[47]  Colin S. Chung,et al.  Decision Tree–Based Deterioration Model for Buried Wastewater Pipelines , 2013 .

[48]  Rossi,et al.  Criticality and Risk Assessment for Pipe Rehabilitation in the City of Santa Barbara Sewer System , 2015 .