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Record W2955171787 · doi:10.29173/mocs92

The Value Proposition of RFID Technology in Tall Prefabricated Timber Buildings

2019· article· en· W2955171787 on OpenAlexvenueno aff
Perry Forsythe, Alireza Ahmadian Fard Fini, Alireza Jalali Yazdi

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsPrefabricationRadio-frequency identificationSupply chainFactory (object-oriented programming)Value propositionArchitectural engineeringProduction (economics)Investment (military)Order (exchange)EngineeringComputer scienceOperations managementManufacturing engineeringSystems engineeringBusinessCivil engineeringComputer securityMarketing

Abstract

fetched live from OpenAlex

The full benefit of prefabricated timber systems in the construction of multi-story buildings depends on integration and efficiency in the upstream logistics and supply chain. The purpose of this research is therefore to determine the potential value that the use of Radio-Frequency Identification technology (RFID) can contribute to the prefabricated construction of timber, and to undertake the basic development of a RFID tracking model for this purpose. The methods used in this study not only build on the knowledge gained from previous literature, but also include interviews with industry experts, field trial design and field trials. The research showed that the RFID tracking system's value proposition tends to be strongest where there are large scale and vertically integrated supply chains, logistics complexity between a limited number of discrete but partnered supply chain links and/or internal logistical complexity problems. Therefore, five distinct added value stages of RFID applications have been found in incoming delivery logistics, factory panel production, outgoing delivery logistics, on-site installation and third parties who can inspect the finished construction work. Application of RFID technology in prefabrication factory environments, where fixed readers can be used in predefined processes, was found promising. However, due to the temporary nature of the sites and the associated investment, the capacity for high automation levels is thought to be more limited on site.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.651

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 head0.003
GPT teacher head0.172
Teacher spread0.169 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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