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Record W2321010814 · doi:10.1139/cjce-2013-0103

Modeling the impact of work-zone traffic flows upon concrete construction: a high level architecture based simulation framework

2013· article· en· W2321010814 on OpenAlexafffundvenue
Md. Hadiuzzaman, Yang Zhang, Tony Z. Qiu, Ming Lu, Simaan AbouRizk

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsStantec (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScheduleComputer scienceWork (physics)Domain (mathematical analysis)Traffic flow (computer networking)ArchitectureResource (disambiguation)Traffic congestionTransport engineeringConstruction engineeringCivil engineeringEngineeringComputer network

Abstract

fetched live from OpenAlex

Congestion can restrict access to construction activities both on and close to a freeway, reducing construction efficiency, especially by delaying the delivery of materials. Although previous studies have investigated the impact of work-zone capacities on traffic flow, most did not consider interactions with construction resources. There is currently no mature standard or acceptable model that can be used for this purpose, and that would benefit both traffic and construction in an integrated framework; however, well-established, powerful simulation systems exist unique to each domain. To this end, in this paper, a construction-traffic interdisciplinary simulation (CTISIM) framework based on high level architecture is proposed to combine the power of each domain’s simulation systems. A ready-mixed concrete production and delivery problem is adopted to demonstrate the benefit of the CTISIM, which optimizes construction resource arrangement and a concrete production schedule based on the real-time traffic conditions in a work-zone.

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 score0.503
Threshold uncertainty score0.576

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 integrity0.0000.001
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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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

Citations6
Published2013
Admission routes3
Has abstractyes

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