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Record W7027891210

Design for manufacturing and assembly (DfMA) and challenges of its application in on-site construction (OnSC)

2024· other· en· W7027891210 on OpenAlexfundno aff

Bibliographic record

VenueEspace École de technologie supérieure (École de technologie supérieure) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersMitacs
KeywordsSystematic reviewField (mathematics)Identification (biology)Construction industryWork (physics)Design for manufacturability
DOInot available

Abstract

fetched live from OpenAlex

The construction industry has long had to deal with low productivity, prompting a search for innovative solutions. Off-site construction (OSC) has emerged as a promising option to enhance productivity, and the integration of Design for Manufacturing and Assembly (DfMA) principles has gained considerable attention in recent years. While DfMA is expected to find widespread adoption in OSC, it holds potential benefits for both on-site and OSC activities. However, there is a notable lack of research comparing DfMA practices in on-site construction. \n \nThe present article-based thesis delves into the misconception that DfMA exclusively serves OSC projects. Using the Design Science Research (DSR) methodology, we recognize that various construction projects, including on-site components of OSC projects, can still benefit from DfMA principles. As DfMA's systematic adoption proliferates across the construction sector, it is essential to identify and address challenges associated with its implementation at diverse construction stages. The literature review initially assessed the current state of DfMA adoption in construction, spanning OSC and on-site contexts, with a bibliometric analysis to explore their relationship. The findings from this literature review offer valuable insights, including an in-depth discussion of DfMA in OSC and on-site construction, identification of research gaps, and recommendations for future developments in this field. After that we focused on the on-site aspects of construction, examining and analyzing 42 validated DfMA challenges grouped into nine main categories. These findings inform the development of a DfMA-related challenges framework. By comprehensively identifying and understanding these challenges across OSC and on-site construction, this study contributes to the construction management field and provides valuable insights for industry professionals, researchers, and policymakers. Ultimately, it offers guidance for organizations aiming to implement DfMA strategies effectively. This will enhance construction productivity, sustainability, and competitiveness in the built environment.

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)0.010
metaresearch head score (Gemma)0.016
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: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.004
Scholarly communication0.0080.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.279
Teacher spread0.248 · 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
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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