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Record W1967849422 · doi:10.1139/l01-032

Integrating CAD and schedule for identification and resolution of work space conflicts between subcontractors

2001· article· en· W1967849422 on OpenAlexvenueno aff
Sy-Jye Guo

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersNational Science Council
KeywordsScheduleComputer scienceScheduling (production processes)Identification (biology)Space (punctuation)Critical path methodConflict resolutionOperations researchCADIndustrial engineeringSpace explorationSoftwareSystems engineeringRisk analysis (engineering)EngineeringOperations managementEngineering drawing

Abstract

fetched live from OpenAlex

Numerous workers, equipment, material, temporary facilities, and permanent structures share the limited space during construction. Since space constraints may affect productivity and the critical path, it is essential to organize the available space efficiently and minimize space conflicts. This study considers space availability due to time and scheduling, productivity loss due to space constraints and path interference, as well as the possibility of alternative space to resolve these conflicts and optimize space usage. Herein CAD is integrated with scheduling software for dynamic identification of space conflicts on the job site. Follow-up supplemental decision criteria are then provided for conflict analysis and resolution. A prototype decision support system, which combines the criteria, was developed to solve this significant and complex problem more efficiently and precisely. A case study demonstrates the use and development of this system, which is very helpful to engineers and project management.Key words: space, conflict, resolution, CAD, schedule.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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 designNot applicable
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
Published2001
Admission routes1
Has abstractyes

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