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Record W2163340299 · doi:10.1139/l07-048

Equipment workspace analysis in infrastructure projects

2007· article· en· W2163340299 on OpenAlexfundvenueaboutno aff
Amin Hammad, Cheng Zhang, Mohamed Al‐Hussein, Germain Cardinal

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWorkspaceBridge (graph theory)Computer scienceConstructiveKey (lock)Representation (politics)Construction engineeringFlexibility (engineering)Risk analysis (engineering)Systems engineeringEngineeringCivil engineeringRobotProcess (computing)BusinessComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

Workspace conflicts are one of the important problems that can delay construction activities, reduce productivity, or cause accidents that threaten the safety of workers. Workspace planning is particularly important in the case of large infrastructure projects, such as bridge construction, and rehabilitation projects where equipment with complex workspace requirement is required. This paper aims to extend the previous research on workspace representation and analysis in the case of large infrastructure projects focusing on the following issues: (i) specific representation of equipment workspaces using composite shapes, (ii) semiautomatic generation and analysis of workspaces, and (iii) development of a prototype system that can generate workspaces and detect spatiotemporal conflicts in a three-dimensional environment. The computational aspects of the proposed approach are discussed and demonstrated through the development of a prototype system, which has been applied in a case study of the deck replacement of Jacques Cartier Bridge in Montréal.Key words: workspace analysis, spatiotemporal conflicts, construction equipment, infrastructure projects, bridge redecking, constructive solid geometry.

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: none
Teacher disagreement score0.684
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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

Citations21
Published2007
Admission routes3
Has abstractyes

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