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Record W4394956535 · doi:10.3390/buildings14041151

Design Analysis of Mass Timber and Volumetric Modular Strategies as Counterproposals for an Existing Reinforced Concrete Hotel

2024· article· en· W4394956535 on OpenAlexafffundabout
M.C. Filion, Sylvain Ménard, Carlo Carbone, Mohamad Bader Eddin

Bibliographic record

VenueBuildings · 2024
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designStructural engineeringEngineeringComputer scienceArchitectural engineeringCivil engineeringProgramming language

Abstract

fetched live from OpenAlex

Construction professionals work in silos and use traditional design and construction methods. The growing demand for rapidly built and high-quality construction is making off-site manufacturing mainstream. Studies have shown that collaboration among all stakeholders is a necessary component for success in the construction of such buildings. This multidisciplinary study of an existing concrete hotel aims to explore an alternative structural design in mass timber or volumetric modular construction. To this end, the reinforced concrete floor plan of Club Med de Charlevoix in Quebec, Canada, was used as a benchmark for two different structural systems. The first strategy investigated CLT (cross-laminated timber) and glulam columns to replicate the reinforced concrete system (column–slab), while the second involved maximum prefabrication (volumetric modular construction). Both mass timber and volumetric modular strategies can lead to a smaller carbon footprint. The main conclusion is that the plan should be designed from the outset to be either traditional or prefabricated since major changes are required if the choice is made to switch from one system to the other. Moreover, when structural systems maximize off-site construction, such as volumetric modular construction, the various professions need to be included during early planning. This is necessary to avoid task duplication and prevent the neglect of considerations such as manufacturable dimensions and partition organization.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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 designTheoretical or conceptual
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

Citations7
Published2024
Admission routes3
Has abstractyes

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