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Record W2316434383 · doi:10.1061/40976(316)673

Life-Cycle Cost Based Rehabilitation Plan for Water Mains

2008· article· en· W2316434383 on OpenAlexaffabout
Khaled Shahata, Tarek Zayed

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2008 · 2008
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsRehabilitationTrenchComputer scienceEngineeringMarine engineeringEnvironmental scienceMaterials science

Abstract

fetched live from OpenAlex

According to the Canadian National Research Council reports, the renewal and rehabilitation of infrastructure across Canada is estimated to be at least $15 billion. Life cycle cost is an essential approach to distinguish alternative rehabilitation strategies for water main rehabilitation. Therefore, Life-cycle cost is used to compare different alternative strategies among water main rehabilitation techniques. Current research identifies several rehabilitation methods for water mains, which are classified into three main categories: (1) repair (e.g. open trench, sleeves); (2) renovation (e.g. slip lining, cement lining, epoxy lining, CIPP); and (3) replacement (e.g. pipe bursting, micro-tunneling, directional drilling, auger boring, open cut). Stochastic life cycle cost (SLCC), using Monte Carlo simulation approach, is used to compare different rehabilitation scenarios within the same alternative. Data, related to the cash flow of each scenario, are collected from contractors and municipalities in Canada. Results show that using "Open Trench" and "Slip-Lining" are the best methods for "repair" and "renovation" categories, respectively. However, the best method for "replacement" category is pipe bursting for small pipe diameters (<30") and open cut for large pipe diameters (>30"). Accordingly, a rehabilitation plan, based on SLCC, favors repairing with "Open Trench" until the breakage rate reaches 0.5 breaks/ km/year then replaces the main.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.512

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 head0.162
Teacher spread0.155 · 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

Citations3
Published2008
Admission routes2
Has abstractyes

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