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Record W3210522541 · doi:10.1139/cjce-2020-0429

Generating labour cost budget for a construction-oriented fabrication facility: simulation-based resource scheduling approach

2021· article· en· W3210522541 on OpenAlexafffundvenue
Leila Zahedi, Ming Lu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScheduling (production processes)CrewProductivityOperations researchProcurementDiscrete event simulationResource (disambiguation)Computer scienceResource planningOperations managementEngineeringEconomicsSimulationEnvironmental resource management

Abstract

fetched live from OpenAlex

A novel labour-hour budgeting method is proposed by integrating productivity concepts in project scheduling and budgeting to enhance the accuracy of labour cost budgeting for planning labour-intensive projects. The proposed method applies discrete event simulation approach to represent crew formation, labour resource utilization, and labour resource flowing between consecutive activities, which allows for quantitatively characterizing the impact of labour semi-productive time on labour cost budgeting as a time-dependent variable. Simulation-based assessment of variations in crew sizes and labour allocations is conducive to reducing semi-productive time and thus enhancing the cost performance of the whole project. The proposed method is then applied in a real-world case study for planning steel girder fabrication projects in construction of highway bridges. Not limited to budgeting for labour resources in construction-oriented fabrication facility, the research contributions are also significant to other construction planning settings where limited resources are shared and utilized among different activities.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.197
Teacher spread0.187 · 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
GenreMethods

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

Citations6
Published2021
Admission routes3
Has abstractyes

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