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Record W2135726511 · doi:10.1139/l10-108

Scheduling model for repetitive construction processes for high-rise buildings

2010· article· en· W2135726511 on OpenAlexvenueno aff
Kyuman Cho, Taehoon Hong, Chang-Taek Hyun

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsScheduling (production processes)Computer scienceIndustrial engineeringConstruction managementTwo-level schedulingDistributed computingEngineeringDynamic priority schedulingScheduleOperations managementCivil engineering

Abstract

fetched live from OpenAlex

In a high-rise building project that involves many repetitive construction processes, the effective control of such repetitive construction processes is a key factor in the success of the project. Unlike the existing scheduling methods for repetitive construction processes, a scheduling model that considers (i) the flexible job logic of the multiple work tasks that comprise a construction process and (ii) the productivity of the construction equipment and labor was developed in this paper. Various theories and algorithms such as the linking dummy, the linear scheduling method, and the production rate, were implemented in the development of the model. A scheduling model was then proposed based on such theories and algorithms and on a mathematical formula. The proposed model was verified by applying it to core wall construction, a key repetitive process in a high-rise building project. Verification showed that the developed model exhibited over 90% reliability. It is expected that the proposed model will allow effective scheduling for repetitive construction processes with flexible job logic.

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 score0.822
Threshold uncertainty score0.999

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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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
Published2010
Admission routes1
Has abstractyes

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