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Record W3036218685

Integrated Asset Management Framework and Model for Water Distribution Networks

2020· dissertation· en· W3036218685 on OpenAlexaboutno aff
Hadi Ganjidoost

Bibliographic record

VenueUWSpace (University of Waterloo) · 2020
Typedissertation
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsAsset managementAsset (computer security)Distribution (mathematics)Computer scienceBusinessMathematicsFinanceComputer security
DOInot available

Abstract

fetched live from OpenAlex

The Canadian Infrastructure Report Card (2012) estimates the replacement value of water assets to be $362 billion. Water distribution and wastewater collection networks have been in service for more than a century in the majority of the cities in Canada. Although “out of sight” infrastructure might often be “out of mind”, the functionality of these city arteries greatly influences public health. Lack of effective maintenance and proactive renewal plans increase the incurred costs of water infrastructure systems drastically until affordable water fees cannot cover them. The Sustainable Water and Sewage System Act (MEO, 2002) followed by the Water Opportunities and Water Conservation Act (MEO, 2010), both encourage public utilities to develop financially sustainable plans for water and wastewater systems. In addition, both Ontario Regulation 453/07 (MEO, 2007) and Public Sector Accounting Board (PSAB) Statement 3150 (CICA, 2007) require all public water utilities to prepare annual reports on the current and the future condition of their in-service assets. Managing aging water infrastructure systems with limited financial resources requires comprehensive asset management plans that help decision-makers minimize the total life-cycle cost of their assets while enhancing levels of service. A viable asset management plan should incorporate a Strategic plan (10+year), to set the policies and strategies; Tactical plan (2-10 years), to develop capital programs; and Operational plan (1-2 years), to establish capital projects. Effective dynamic communication among planning levels is critical to share and exchange information and, thus, promote alignment of their respective objectives.
\nThis research develops an Integrated Water Infrastructure Asset Management (IWIAM) model comprised of strategic, tactical and operational plans to (1) align corresponding objectives; (2) share and exchange their information; and (3) optimize the allocation of financial resources.
\nA novel hybrid Agent-Based and System Dynamics (AB-SD) modelling approach is employed to develop an IWIAM for water distribution networks. The SD and AB models are used to understand the complex dynamic behaviour of water infrastructure systems for network-level (i.e., strategic) and component-level (i.e., tactical-operational), respectively. A four-step Plan-Do-Check-Adjust (PDCA) iterative management process, along with an integrated Water Infrastructure Database (WIDB) is utilized to provide effective interaction and communication among all three planning levels. The research applies a bi-level heuristic optimization algorithm to find optimal solutions to group renewal activities in the development of capital programs.
\nThe proposed research makes several noteworthy contributions to the body of knowledge for water distribution networks: 
\n(1) The development of an integrated decision-support system using Agent-Based and System Dynamics methods to aid water decision-makers in asset management planning;
\n(2) The development of a platform for interactions between the network- and component-levels to align network-centric with component-centric decisions; 
\n(3) The development of an optimization model to select, group, and schedule optimal R&R activities;
\n(4) the development of a bi-level heuristic optimization algorithm to find optimal solutions for group scheduling of capital works.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.693
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.172
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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