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Record W4382246289 · doi:10.18280/ijsdp.180619

Exploring the Challenges and Opportunities of BIM Implementation in Major Architectural Projects in Iraq

2023· article· en· W4382246289 on OpenAlexvenueno aff
Munaf Fouad Hassan, Sajeda Kadhum Al-Kindy

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectural engineeringBuilding information modelingEnvironmental planningEngineeringConstruction engineeringBusinessEngineering managementEnvironmental resource managementOperations managementEnvironmental science

Abstract

fetched live from OpenAlex

The economic, environmental, and societal impact of major architectural projects highlights the need to study the challenges that affect their performance and completion.Building Information Modeling (BIM) is a critical tool for enhancing project completion in the AEC field and has been widely used globally, especially in major projects.This research aims to explore the application status of BIM in major architectural projects in Iraq and investigate the reasons behind its limited use.The research methodology involves conducting interviews with specialists and using designed questionnaires to gather feedback from clients, consultants, contractors, and BIM practitioners.Several Iraqi major projects that implement BIM technology are selected as case studies.The research findings reveal that BIM application levels in the design and construction of major projects in Iraq are limited due to several reasons, with the main reason being the lack of government and private institutions' requirements for BIM application in their project designs and construction.This study on BIM's role and application obstacles in major projects in Iraq will serve as a guide for managers and practitioners to determine the appropriate "Level of Detailing" based on project characteristics and integration limits, enabling better benefits for owners and stakeholders.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.096
GPT teacher head0.280
Teacher spread0.184 · 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 designOther design
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

Citations5
Published2023
Admission routes1
Has abstractyes

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