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Record W3158234369 · doi:10.19255/jmpm02511

Exploring the Influence of Risks in BIM Implementation: A Review Exploring BIM Critical Success Factors and BIM Implementation Phases

2021· review· en· W3158234369 on OpenAlex
Tássia Farssura Lima da Silva, Aline Valverde Arrotéia, Darli Rodrigues Vieira, S. B. Melhado, Marly Monteiro de Carvalho

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEspace ÉTS (ETS) · 2021
Typereview
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsBuilding information modelingInteroperabilityRisk analysis (engineering)Process managementRisk managementComputer scienceInterface (matter)Knowledge managementBusinessEngineeringOperations management

Abstract

fetched live from OpenAlex

The adoption of building information modeling (BIM) has a strong potential to influence project performance positively. However, the implementation and use of BIM also involve challenges and risks that must be considered for its practice's success. This study aims to identify gaps and future research direction within the field of BIM and risk management. Besides, it explores the relationship between risks related to BIM implementation and project success dimensions. For this, a literature review is applied, merging bibliometric and content analysis. The results show that the three most frequently mentioned risks are technological interface among programs, followed by interoperability issues, and inadequate knowledge or expertise. Besides, insights pinpoint the positive relation between the BIM critical success factors and the risks associated with BIM, particularly in the design phase.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.187
GPT teacher head0.415
Teacher spread0.229 · 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