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Record W4296077796 · doi:10.29173/mocs285

VDC management in the industrialization process using prefabricated reinforcement cages. Case Study: Ovalo Monitor Bridge

2022· article· en· W4296077796 on OpenAlexvenueno aff
Alejandro Palpan, Sandra Vega, Felipe Quiroz Quiroz, Mark Vicuña, Rodrigo Tuesta, Alexandre Almeida Del Savio

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2022
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsConstructabilityIndustrialisationBridge (graph theory)PrefabricationEngineeringConstruction engineeringProcess (computing)Civil engineeringStructural engineeringComputer science

Abstract

fetched live from OpenAlex

Reworks and delays in production processes are commonly found in construction projects associated with a low level of industrialization and a lack of design constructability. To promote industrialization, improve project constructability, and reduce the execution time, we implemented the Virtual Design and Construction (VDC) methodology. An industrialization strategy was established using a prefabricated reinforcement cages system (PRC) elements in an 870-meters bridge construction project in Lima, Peru. The objective was to improve the project buildability with high industrialization of the steel rebar works. We replaced the traditional on-site cutting and bending steel reinforcement processes with an industrial process that integrates construction management with the supply chain through Building Information Model (BIM). As a result, the level of industrialization of the PRC elements of the bridge substructure and superstructure reached 85% and 40%, respectively, aligned with a 16% execution time reduction of the project.

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 score0.404
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.249
Teacher spread0.222 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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