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Record W2939569709 · doi:10.29173/mocs31

Prefabricated Prefinished Volumetric Construction Joining Tech-niques Review

2016· article· en· W2939569709 on OpenAlexvenueno aff
Sze Dai Pang, J.Y. Richard Liew, Ziquan Dai, Yanbo Wang

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2016
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersNational Research Foundation SingaporeNational University of SingaporeNational Research FoundationSembcorp Industries
KeywordsConstructabilityModular designComputer scienceConstruction engineeringEngineeringSystems engineering

Abstract

fetched live from OpenAlex

Prefabricated Prefinished Volumetric Construction (PPVC), also known as volumetric modular construction has recently been adopted for high-rise building construction. However, efficient ways of joining the volumetric units on site have not been well developed. The intended high productivity is thus hindered and costs of the PPVC buildings are considerably higher than traditional construction methods. Additionally, issues such as module alignment and water penetration are often exposed during the assembly of modules. Moreover, local code requirements and geographical varieties often lead to different priorities and concerns in the development of joining techniques. This paper focuses on the generic joining techniques adopted in PPVC systems. Three joining methods are presented based on the different locations where the tightening of bolted connection occurs. The methods are evaluated based on the proposed constructability criteria and structural performance. The information gathered is useful for designers and contractors to understand the priorities and issues when developing new joining techniques.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.008
GPT teacher head0.194
Teacher spread0.185 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations37
Published2016
Admission routes1
Has abstractyes

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