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Record W2133129963 · doi:10.5555/1516744.1517180

Simulation as a tool for life cycle cost analysis

2008· article· en· W2133129963 on OpenAlexaff
Khaled Shahata, Tarek Zayed

Bibliographic record

VenueWinter Simulation Conference · 2008
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsLife cycle costingMains electricityRehabilitationComputer scienceLife-cycle cost analysisMonte Carlo methodElectricityOrder (exchange)Risk analysis (engineering)Reliability engineeringOperations researchEngineeringOperations managementBusiness

Abstract

fetched live from OpenAlex

Life cycle cost is an essential approach to decide on alternative rehabilitation strategies for infrastructure systems. Monte Carlo simulation approach is used to develop a stochastic life cycle cost (SLCC) model and methodology in order to compare different rehabilitation scenarios/ alternatives for infrastructures, such as water mains. The presented research in this paper identifies several rehabilitation methods for water mains, which are classified into three main categories: repair, renovation, and replacement. The developed model helps academics and practitioners (e.g. municipal engineers) to predict the suitable new installation and/or rehabilitation programs as well as their corresponding costs, thereby, to avoid any unpleasant surprises.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.033
GPT teacher head0.264
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2008
Admission routes1
Has abstractyes

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