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Record W6923603382 · doi:10.14288/1.0434148

Understanding the changes resulting from the virtualization of BIM-enabled collaborative design processes in the building construction industry

2023· article· en· W6923603382 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2023
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsBuilding information modelingContext (archaeology)MaintainabilityVirtualizationUsabilityCollaborative designIndustry 4.0Key (lock)Design technology

Abstract

fetched live from OpenAlex

The Canadian construction industry contributes significantly to the country's economy but faces various challenges, such as inefficiency, delayed projects, and negative environmental impact. Building Information Modelling (BIM) has been identified as a key driver for the industry's digital transformation and a potential remedy to its challenges. However, the adoption of BIM has been limited in the construction industry, which remains among the least digitalized sectors. Enabling digitalization in the construction industry, such as adopting BIM-enabled tools, requires change management competency. This study aims to understand how the virtualization led by BIM-enabled tools changes building design processes, specifically collaborative design processes. The study investigates three collaborative design processes with increasing complexity levels that employ various BIM tools. The impact of BIM-enabled tools on the collaborative design processes of maintainability design reviews, multi-disciplinary design coordination, and lean design management was investigated in this study. These design processes were studied within the context of different real-world case study projects that were selected to ensure diverse collaborative environments and contractual structures. The study utilized an action research approach, and mixed methods of data collection consisting of direct observations, interviews, and document and model analysis were used. The findings suggest that adopting BIM-enabled tools can improve collaborative design processes by enhancing communication, increasing efficiency, and improving information exchange between project stakeholders. The contributions of this study are threefold—first, the usability of virtual reality tools to improve communication and decision-making for maintainability design reviews is empirically demonstrated. Second, a framework for categorizing the changes from adopting cloud-based collaboration tools is developed and operationalized to illustrate the impact of cloud-based collaboration tools on the design coordination process. Finally, empirical evidence and practical insights are provided into the efficacy and challenges of executing lean design management processes in completely remote and virtual collaborative work environments. The findings from this study can help design professionals and project managers better plan and implement BIM-enabled tools in practice and enable the virtualization of design processes in the building construction industry.

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)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.009
Scholarly communication0.0130.008
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.195
Teacher spread0.160 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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