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Record W2918507475 · doi:10.1139/cjce-2018-0072

A framework to assess the costs and benefits of advanced work packaging in industrial construction

2019· article· en· W2918507475 on OpenAlexafffundvenue
Yonas Samuel Halala, Aminah Robinson Fayek

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversity of AlbertaCanadian Natural Resources
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsScheduleWork (physics)Process (computing)ProductivityRisk analysis (engineering)Cost–benefit analysisEngineeringComputer scienceBusiness

Abstract

fetched live from OpenAlex

The term advanced work packaging (AWP), coined by the Construction Industry Institute (CII), refers to a disciplined process for project planning and execution; it was developed to address challenges such as cost and schedule overruns in the industrial construction sector. Case studies conducted on AWP report a number of benefits in the areas of productivity, cost, safety, and schedule. However, since there is no clear method to assess the costs and benefits of AWP implementation, a significant challenge in AWP adoption is the lack of quantitative evidence to support these reported benefits. This paper presents a structured framework to assess multiple aspects of AWP implementation, which will enable the quantification of both its costs and benefits. The framework will enable the future comparison of AWP and non-AWP projects. This paper contributes to the industrial construction sector by providing a framework to assess AWP implementation in practice.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.056
GPT teacher head0.283
Teacher spread0.227 · 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 designObservational
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

Citations18
Published2019
Admission routes3
Has abstractyes

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