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Record W2804760574 · doi:10.29173/mocs2

A comparison between Volumetric Timber Manufacturing Strategies in the UK and mainland Europe

2016· article· en· W2804760574 on OpenAlexvenueno aff
Tsvetomila Duncheva, Fiona F. Bradley

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2016
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersUniversity of Strathclyde
KeywordsContext (archaeology)Variety (cybernetics)Flexibility (engineering)ProductivityPrefabricationMainlandProduction (economics)Quality (philosophy)Factory (object-oriented programming)Greenhouse gasLegislationEngineeringBusinessArchitectural engineeringEnvironmental economicsOperations managementCivil engineeringComputer scienceEconomics

Abstract

fetched live from OpenAlex

The construction sector in the UK is currently being challenged by an urgent need to produce housing. This requirement is within a context of increasingly stringent greenhouse gas emission legislation and tough goals to reduce build time by 50% and financial cost by 33% by the year 2025. Many agree that these targets and others relating to build quality and productivity can only be achieved with increased utilisation of Modern Methods of Construction (MMC). This research study proposes that Volumetric Timber construction (VT) is a suitable system for use in the UK, as it can achieve up to 95% prefabrication in a controlled factory environment combined with rapid onsite construction. There are a variety of VT timber systems on the market, including timber studs, Cross Laminated Timber (CLT) and Structurally Insulated Panel (SIP) structures and a variety of production methods. This paper presents findings from a study comparing four VT manufacturers in the UK and four in mainland Europe. Different design and processing methods have been compared and analysed with respect to production type, offsite components and productivity metrics. The findings demonstrate that there are diverse approaches to efficient VT manufacturing and opportunities for flexibility in the design of products to suit local market conditions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.578

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 communication0.0000.001
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 head0.011
GPT teacher head0.216
Teacher spread0.204 · 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 designObservational
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

Citations3
Published2016
Admission routes1
Has abstractyes

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