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Record W2143084847 · doi:10.1080/01446190110066713

Schedule-dependent evolution of site layout planning

2001· article· en· W2143084847 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConstruction Management and Economics · 2001
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsScheduleComputer scienceSite planningEngineeringEngineering drawingOperating system

Abstract

fetched live from OpenAlex

The appropriate layout of temporary facilities on a construction site has a large impact on construction safety and productivity. For the duration of a project the site layout may need to be efficiently re-organized at various intervals to satisfy the schedule requirements and to maintain site efficiency. This paper presents a practical model for schedule-dependent site layout planning in construction. The proposed model uses a combination of artificial intelligence tools (knowledge-based systems, fuzzy logic, and genetic algorithms) to generate, optimize, and re-organize the site layout plan at frequent intervals during the project. The model incorporates flexible representation of irregular site shapes and several options for placing facilities. Based on the proposed model, an automated system is developed, fully integrated with widely used scheduling software. At each schedule interval, the system recalculates the space requirements and, for the convenience of congested sites, can utilize parts of the constructed space to accommodate temporary facilities. Details of the schedule-dependent model are described, and its application in an actual case study project is presented to demonstrate its capabilities.

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.

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: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.322

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 head0.008
GPT teacher head0.185
Teacher spread0.177 · 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