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Record W4380449378 · doi:10.52202/069179-0560

STUDY CASE: REFURBISHMENT OF THE GARE MARITIME IN BRUSSELS

2023· article· en· W4380449378 on OpenAlexaboutno aff
Charline Lefèvre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Architectural engineeringRoofProcess (computing)Quarter (Canadian coin)EngineeringEngineering design processFocus (optics)Civil engineeringConstruction engineeringComputer scienceTransport engineeringMechanical engineeringGeography

Abstract

fetched live from OpenAlex

This paper presents the study case of the Gare Maritime, a former train station in Brussels in Belgium that covers an area of 40.000m and that has been entirely renovated to welcome under its roof the construction of twelve multilevel pavilions in fully timber structure. Those 4500 m of twelve new buildings are hosting shops, publics functions and offices and creates a new covered quarter inside the city. This paper introduces first the project, its historical context, its situation and its refurbishment. Then an explanation of the structural design is given and divided in different parts. A focus is first made over the specific features of the project to explain the challenges of the structure. Then the joints developed on the project are explained explaining the main type of assemblies. Thirdly, all the fire engineering process of the project is described; how the dossier was accepted by the authorities, the simulations and calculations that needed to be done. The last section finally gives information about the mounting process and the upstream preparation that it required. In the next chapter, more can be learned over the innovative technologies that have been implemented on the project to make it completely energy neutral and fossil free before finally concluding and giving some important numbers over 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.149

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 studies0.0000.000
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 head0.012
GPT teacher head0.234
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

Citations0
Published2023
Admission routes1
Has abstractyes

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