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Maximizing project efficiency and collaboration in construction management through Building Information Modeling (BIM)

2024· article· en· W4401032683 on OpenAlex

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

VenueApplied and Computational Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBuilding information modelingStakeholderWorkflowProcess managementProfitability indexProject managementBusinessReturn on investmentKnowledge managementComputer scienceSystems engineeringEngineeringOperations management

Abstract

fetched live from OpenAlex

Building Information Modeling (BIM) represents a transformative approach in construction management, significantly enhancing project efficiency, stakeholder collaboration, and economic performance. This paper examines the integration of BIM across different phases of the construction project lifecycle, including pre-construction planning, resource allocation, and risk management. Utilizing quantitative analyses and empirical data, we explore how BIM facilitates precise planning, optimizes resource usage, and proactively manages project risks. Furthermore, the paper discusses BIM’s pivotal role in improving stakeholder communication, coordinating workflows, and enhancing decision-making processes. By detailing BIM’s impact on cost reduction, time savings, and return on investment, the study highlights its capacity to drive financial performance and stakeholder satisfaction in construction projects. The findings suggest that BIM not only streamlines project management but also significantly boosts profitability and efficiency.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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