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Record W58792425 · doi:10.22260/isarc2003/0041

Open Architecture for Site Layout Modeling

2003· article· en· W58792425 on OpenAlexaff
Farnaz Sadeghpour, Osama Moselhi, Sabah Alkass

Bibliographic record

VenueProceedings of the ... ISARC · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceArchitecturePlan (archaeology)Object (grammar)Floor planSelection (genetic algorithm)Building information modelingSoftware engineeringEngineering drawingEngineeringArtificial intelligenceScheduling (production processes)

Abstract

fetched live from OpenAlex

This paper presents an overview of a computer-based site layout model and focuses primarily on the project setup phase.The developed model has four modules: user interface, database, project module, and layout module.Setting up the project in the proposed model is carried out by the project module, utilizing open architecture concept.The main advantage in the open architecture is to allow for the incorporation of userdefined objects if they are not readily available in the model.The objects required to define a site layout problem are clustered into three tires: 1) Site Objects, 2) Construction Objects, and 3) Constraint Objects.The model is implemented in a CAD environment using an object-based approach.The structure of each of the three tires is described, and the mechanism of object selection/creation for a site layout project is explained.The paper describes the components required to implement an open architecture for site layout object selection along with their respective environments.The developed model can be easily extended to similar applications such as floor plan design.

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)0.001
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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
GenreMethods

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

Citations5
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... ISARCSame topicAdvanced Manufacturing and Logistics OptimizationFrench-language works237,207