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Record W3101363250 · doi:10.1061/9780784482889.126

Guidance to Safety-Centric Construction Acceleration Planning in the Context of Project Time-Cost Tradeoff Analysis

2020· article· en· W3101363250 on OpenAlexaff
Samin Mahdavian, Kumar Subramanian Bellale Manjunatha, Estacio Pereira, Ming Lu

Bibliographic record

VenueConstruction Research Congress 2020 · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDeliverableContext (archaeology)AccelerationComputer scienceWork (physics)Duration (music)Resource (disambiguation)Risk analysis (engineering)Operations researchTransport engineeringEngineeringSystems engineeringBusiness

Abstract

fetched live from OpenAlex

The time-cost tradeoff (TCT) analysis is intended to expedite critical activity times and total project duration, possibly resulting in more hazardous situations and more safety risks than the normal case. On the other hand, safety-centric construction acceleration planning is complicated and challenging as a result of dynamic work settings, rotation of various work teams, exposure to changing weather conditions, and employing higher proportions of inexperienced workers. In connection with TCT theory, this study is aimed to assist construction planners in implementing safety-centric planning for resource use, time, and cost at critical activities. In order to guide construction acceleration planning while minimizing safety hazards and preventing accidents, this research consulted published literature, investigated best practices, and referenced regulations related to occupational health and safety. The research deliverable includes compilation of significant factors and generalization of a set of rules for facilitating construction acceleration planning and enabling the follow-up TCT analysis.

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)0.025
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.025
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0030.004
Scholarly communication0.0110.006
Open science0.0030.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0110.003

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 head0.189
GPT teacher head0.509
Teacher spread0.320 · 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 designTheoretical or conceptual
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

Citations2
Published2020
Admission routes1
Has abstractyes

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