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Record W2406724119 · doi:10.1139/cjce-2015-0144

Conflicts resolution based construction temporary facilities layout planning in large-scale construction projects

2016· article· en· W2406724119 on OpenAlexvenueno aff
Xiaoling Song, Jiuping Xu, Charles Shen, Feniosky Peña‐Mora

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicOptimization and Mathematical Programming
Canadian institutionsnot available
FundersSichuan UniversityNational Natural Science Foundation of China
KeywordsProcess (computing)Conflict resolutionScale (ratio)Construction engineeringResolution (logic)Construction managementComputer scienceOperations researchEngineeringTransport engineeringCivil engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The construction temporary facilities layout planning (CTFLP) is important and complex in large-scale construction projects. During the CTFLP process, the general contractor has conflicts with many subcontractors regarding other pre-planning tasks, such as the transport routes planning (TRP). The resolution of the conflicts is crucial and this study attempts to determine the CTFLP considering conflicts resolution. Specifically, a novel methodology based on conflicts resolution between the CTFLP and the TRP is proposed, which can be adapted for future CTFLP based on resolving other types of conflicts. The two procedures-based methodology is composed of a bi-level model and a solution approach. The methodology is applied to a dam construction project in China to test the applicability, and the results show that it is able to obtain a satisfactory CTFLP and prevents some crucial conflicts, thus, promoting construction operations and making a significant contribution to the practice of construction projects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.195
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2016
Admission routes1
Has abstractyes

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