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Advanced deterioration models for wastewater inspection prioritizationNgandu Balekelayi, PhD Candidate
Dr. Solomon Tesfamariam, Professor
School of Engineering, University of BritishColumbia, Okanagan
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Advanced deterioration models for wastewater inspection prioritization
Ngandu BalekelayiDr. Solomon Tesfamariam
School of Engineering, University of British Columbia
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CONTENT
βIntroduction
βSewer pipes deterioration
βRisk based decision making
βValue of information for prioritization of inspection
βConclusion
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INTRODUCTION
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INTRODUCTION
β Wastewater networks systems (sewers):
β’ important and expensive municipal infrastructure assets
β’ Critically support the quality (health, economy) of life of citizen
β 27% of sewer pipes in Canada are in fair to poor condition representing a
total replacement cost of CAD 24billion (Canada Infrastructure Report 2016)
β Limited access to financial resources and stringent regulations
β Prioritization of inspection to cope with financial and service level
requirements
Vojinovic, Z., and Abbott, M. B. (2012). Flood risk and social justice.
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INTRODUCTION
Kley, G., and Caradot, N. 2013. βReview of sewer deterioration modelsβproject SEMAββTechnical report prepared for Kompetenzzentrum Berlin GmbH. Veolia Water, Berlin
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INDIVIDUAL DETERIORATION MODEL
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NONLINEAR MODELING
βSemi parametric model :
ππ = π½0 + π½1π₯π1 +β―+ π½ππ₯ππ + π1 π§π1 + . . . + ππ π§ππ
βP-spline : use of local polynomials in each interval
ππ π§π,π
= πΎπ,1 + πΎπ,2π§π,π +β―+ πΎπ,ππ+1π§π,πππ+ πΎπ,ππ+2 π§π,π β ππ,2 +
ππ+β―+ πΎπ,ππ+βπβ1 π§π,π β ππ,βπβ1 +
ππ
Where π§π,π β ππ,π +ππ= ΰ΅ π§π,π β ππ,π
ππππ π§π,π β₯ ππ,π
0 ππ‘βπππ€ππ π
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GEOADDITIVE REGRESSION MODEL
βGeospatial location as surrogate covariateβ’ Variable location is added to the model to represent unknown variables
ππ = ππππππππ + π1 π§π1 + . . . + ππ π§ππ + ππππ π π
where π π represent the membership of an observation to a given region S
β’ Neighborhood : common boundaries
Z π, π = αα»1 ππ π¦π π€ππ πππ πππ£ππ ππ ππππππ π (π. π. , ππ = π1
0 ππ‘βπππ€ππ π
Source: Fahrmeir et al. (2015)
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Application to large sewer network
π= πΎ0 + πΎ1 β πππ‘πππππ + πΎ2 β πΏππππ‘β + πΎ3 β π·πππππ‘ππ + πΎ4 β π΄ππ + πΎ5 β π·πππ‘β + πΎ6 β πππππ + πΎ7β π π πππ£π + πΎ8 β πΆπ πππ£π + πΎ9 β πΉππ’π βππ + πΎ10 β π ππππππ + πΎ11 β π ππ’π‘π + πΎ12 β π΅ππππ’ππ + πΎ13β π·ππππππ π + πΎ14πΆππππππππ + πΎ14 β π·πππππ‘ππ β πΏππππ‘β + πΎ15 β π·πππ‘β β πππππ + πΎ16 β πΆπππππππβ π΅ππππ’ππ + ππ π‘ππ’ππ‘ πππ π‘ππππ‘ + ππ’ππ π‘ππ’ππ‘ πππ π‘ππππ‘
Balekelayi N and Tesfamariam S. (2019): βStatistical inference of sewer pipes deterioration using Bayesian geoadditive regression modelβ ASCE, Journal of Infrastructure systems (in press).
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Partial effects , model validation and predictions
Balekelayi N and Tesfamariam S. (2019): βStatistical inference of sewer pipes deterioration using Bayesian geoadditive regression modelβ ASCE, Journal of Infrastructure systems (in press).
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Visualization of the geospatial effects
Balekelayi N and Tesfamariam S. (2019): βStatistical inference of sewer pipes deterioration using Bayesian geoadditive regression modelβ ASCE, Journal of Infrastructure systems (in press).
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TOPOLOGICAL RELIABILITY AND CRITICAL COMPONENTS ASSESSMENT
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Implementation on large sewer network
βNo hydrodynamic model available
βPipes defects are not considered in validated models
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Graph theory
Balekelayi, N. and Tesfamariam, S (2019): βGraph theoretic surrogate measure to analyzereliability of water distribution system using Bayesian belief network-based data fusiontechniqueβ ASCE's Journal of Water Resources Planning and Management (in press)
βCentrality metrics:
1. Betweenness : πΆππ΅ =
1
πβ1 πβ2Οπ βπΊ,π β πΟπ βπΊ ,π β π,πβ π
πππ π
πππ
2. Topological centrality :
β’ Network efficiency: πΈ πΊ =1
π(πβ1α»Οπ β π βπΊ
1
πππ
β’ Topological centrality : ππΌπΆ =πΈ πΊ βπΈ πΊβ²
πΈ πΊ
3. Eigenvector centrality: ππ = π΄π
4. Principal component centrality: πΆπ =
(ππ Γ π β ππ Γ πα»(Ξπ Γ 1 βΞπ Γ 1α»
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Implementation on large sewer network
βHydraulic distance:
π =1
ππ β
ΰ΅2 3 π ΰ΅12
π β =π΄
π
πΏβ = ππΏ
βTopological metrics : Betw, EVC, PCC
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Data fusion technique
βOrdered Weighted Averaging (OWA) based data fusion
βObjective function :
πππ₯ π·ππ ππππ πππ π = πππ₯ β
π=1
π
π€πππ π€π
subjected to:
πππππ π π =1
π β 1
π=1
π
π€π π β π
and
0 β€ π€π β€ 1;
π=1
π
π€π = 1
Balekelayi N and Tesfamariam S.: βGraph-theoretic surrogate measure to analyze theperformance of wastewater system using Ordered Weighted Averaging fusion techniqueβ(under internal review).
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Data fusion technique : linguistic degree of optimism
Tesfamariam, S., Sadiq, R., and Najjaran, H. (2010). Decision making under uncertainty - Anexample for seismic risk management. Risk Analysis, 30(1), 78β94.
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Topological reliability and critical components
Betweenness EVC
2020
Topological reliability and critical components
PCC
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Topological reliability and critical components
Conservative Normative
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Topological reliability and critical components
Optimistic
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RISK-VALUE OF INFORMATION BASED INSPECTION PRIORITIZATION FOR LARGE SEWER PIPES
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βRisk definition incompleteness (likelihood x consequence)
βPipe location contributes to its risk
Risk assessment
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Risk based decision making
Likelihood of failureConsequence of failure
Risk map
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Value of information
Decision tree
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Value of information
Probability of getting an alarm during a testing
Probability of getting a silence during a testing
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Value of information
Loss function
πΆπ‘ππ’π‘ββ = π πΉ . πΆπ
Expected cost of the test
Value of Information VoI
πππΌ = πΆπ‘ππ’π‘ββ β πΆβ
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Value of information : Wastewater application
Deterioration model
Probability of failure
Economic criteria
Value of Information
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Value of information : Now
Likelihood of failure
Value of Information
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Value of information after 25 years
Likelihood of failure
Value of Information
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Value of information after 50 years
Likelihood of failure
Value of Information
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βAdvanced deterioration model with geospatial information
βRisk based decision making (rate of deterioration factor)
βValue of information for inspection prioritization
Conclusion
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Thanks
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