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Record W2022308534 · doi:10.1061/40754(183)119

Case Study of Constructability Reasoning in MEP Coordination

2005· article· en· W2022308534 on OpenAlexaff
A. Reza Tabesh, Sheryl Staub‐French

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConstructabilityProcess (computing)Computer scienceBuilding information modelingTable (database)OverlaySystems engineeringIterative and incremental developmentBody of knowledgeKnowledge managementProcess managementSoftware engineeringEngineeringData miningOperations management

Abstract

fetched live from OpenAlex

Building system coordination is a complicated process that requires the detailed layout and configuration of the various building systems such that it complies with design, construction, and operations criteria. Current practice involves an iterative process of sequentially overlaying transparent 2D drawings of each system over a light table to identify potential conflicts and constraints, which is a time-consuming and error-prone process. Recent research efforts have focused on the development of knowledge-based systems to further assist this coordination process. The objectives of this research were to collect and classify data on constructability knowledge utilized as part of a 3D MEP coordination process during design and construction of a complex research facility. We worked with the project team to develop detailed 3D models of all the building systems in several critical spaces. We identified the design and construction knowledge utilized to coordinate these 3D models and classified this knowledge in a multi-variable knowledge framework. The representational framework, 3D modeling process, and modeling constraints are discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.189

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 head0.229
Teacher spread0.220 · 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
Published2005
Admission routes1
Has abstractyes

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