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Record W1986999660 · doi:10.1139/l06-073

A hybrid approach for developing special purpose simulation tools

2006· article· en· W1986999660 on OpenAlexvenueno aff
Yasser Mohamed, Simaan AbouRizk

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceSimulation modelingKey (lock)Systems engineeringAutomationModeling and simulationIndustrial engineeringConstruction managementSimulationSoftware engineeringEngineeringCivil engineering

Abstract

fetched live from OpenAlex

The use of simulation techniques is an effective approach for modeling construction operations. Unfortunately, the high level of technical knowledge and development time required for building functional simulation models renders simulation modeling an impractical technology for many in the construction industry. Research in construction simulation tackles this conflict by providing modeling approaches that reduce the knowledge and time usually required for building simulation models of construction operations. Special purpose simulation (SPS) allows construction engineers with only minimal simulation knowledge to build practical simulation models. This paper presents a hybrid approach (HSPS) for effective and time-saving development of SPS tools. The approach utilizes visual, general purpose modeling elements to customize the simulation behaviors of new SPS elements, minimizing the programming effort required for developing these elements. This paper describes the theoretical background to the HSPS approach, its implementation, and a sample application successfully created subsequently. It also shows the results of an experiment quantifying the savings in development time achieved using this approach.Key words: automation, simulation models, computerized simulation, tunnel construction, construction management.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.487

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 communication0.0000.000
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 head0.012
GPT teacher head0.183
Teacher spread0.171 · 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

Citations16
Published2006
Admission routes1
Has abstractyes

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