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Record W2129994225 · doi:10.5555/2675983.2676380

Assessment of construction operations productivity rate as computed by simulation models

2013· article· en· W2129994225 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWinter Simulation Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsConcordia University
Fundersnot available
KeywordsDiscrete event simulationCredibilityProductivityDuration (music)Simulation modelingProcess (computing)Computer scienceIndustrial engineeringSimulationOperations researchEngineeringMathematics

Abstract

fetched live from OpenAlex

Modeling and simulation tools are used to assist decision-makers to predict essential parameters such as completion duration and productivity rate of construction operations. Two approaches are used, process simulation and system simulation. The first compute parameters based on processes interaction while the second focuses on the complex relationship among project components and their impacts. This paper presents an assessment to simulated project completion duration and productivity rate under traditional Discrete Event Simulation (DES) and modified traditional simulation technique. The evaluation is based on a simulated real case study. The process elements of the case were simulated using (DES) while system elements were simulated using System Dynamics (SD). A significant difference in productivity rate and duration was noticed between the base DES model and the impacted model. The argument presented about the credibility of simulation model outcomes highlight the pitfalls of simulation models and the measures that should be endorsed.

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.

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 score0.680
Threshold uncertainty score0.705

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)0.0010.000

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 head0.021
GPT teacher head0.266
Teacher spread0.245 · 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