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Record W1994119669 · doi:10.1680/mpal.12.00009

The chronographical approach for construction project modelling

2013· article· en· W1994119669 on OpenAlexaff
Adel Francis

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Management Procurement and Law · 2013
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsComputer sciencePlannerGraphicsScheduleSet (abstract data type)Scheduling (production processes)Statistical graphicsRepresentation (politics)Human–computer interactionArtificial intelligenceProgramming languageEngineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

Graphical modelling is considered a suitable approach for displaying project data because of its ability effectively to communicate information. The current scheduling methods seem to be unable, individually, to meet all of the planner’s needs, to be understood visually and to be efficient in terms of displaying as much information as possible. The main purpose of this paper is to present the chronographical approach for planning and monitoring construction projects. The chronographical approach is a more complete communication method, having the ability to alternate from one visual approach to another by manipulation of graphics by way of a set of defined graphical parameters. Each individual approach can help to schedule a certain project type or speciality, show valuable information in a clear and comprehensible manner and facilitate the management of construction site problems visually. Visual communication can also be improved through layering, sheeting, juxtaposition, alterations and permutations, allowing for groupings, hierarchies and classification of project information. In this way, graphical representation becomes a living, transformable image, thus assisting planners in solving problems of a variable nature, and simplifying site management while simultaneously using the visual space as efficiently as possible.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.350

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 head0.010
GPT teacher head0.183
Teacher spread0.173 · 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 designTheoretical or conceptual
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

Citations19
Published2013
Admission routes1
Has abstractyes

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