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Record W3148923809 · doi:10.1109/wsc.2011.6148048

Towards real-time simulation of construction activities considering spatio-temporal resolution requirements for improving safety and productivity

2011· article· en· W3148923809 on OpenAlexafffund
Amin Hammad, Cheng Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsComputer scienceTask (project management)Simulation modelingProductivityData miningReal-time computingSystems engineeringEngineering

Abstract

fetched live from OpenAlex

Traditional simulation models use statistical data to estimate task durations. However, to make the simulation results more realistic and reflecting the changes during the task execution, real-time simulation has been suggested by several researchers. On the other hand, little consideration is given to spatio-temporal constraints in simulation models. Several spatial modeling methods, such as maps, grids and 3D models, have been used in construction simulation. However, different resolutions of spatio-temporal representations should be used based on the specific requirements when considering spatio-temporal conflicts. The present paper aims to propose the basic concept of real-time simulation of construction activities considering spatio-temporal resolution requirements for improving safety and productivity. The objectives of the paper are: (1) to review real-time simulation methods of construction activities considering spatio-temporal conflicts; (2) to investigate the spatio-temporal requirements in the real-time simulation environment; and (3) to investigate the integration of simulation models at different spatio-temporal resolutions.

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: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.236
Teacher spread0.209 · 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

Citations27
Published2011
Admission routes2
Has abstractyes

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