How does building information modeling influence decision-making process in the project design? An input, process and output analysis
Bibliographic record
Abstract
Decision-making is critical throughout the entire project cycle, particularly during the project design stage, where the detailed concept is developed based on stakeholders' requirements and project constraints. To improve project design, various digital technologies are employed to provide stakeholders with comprehensive data for informed decision-making. The paper aims to understand how BIM influence the decision-making process – input, decision and output - during the project design. More specifically, we aim to answer the following question: which decision-making challenges could restrict BIM benefits during the project design stage? Utilizing two embedded case studies and a focus group, we explore the perceived benefits and challenges of BIM in decision-making among project actors. This research contributes to the field of digital technologies in project management by highlighting specific benefits and challenges, such as decision validation and the transformation of decision makers’ roles. Our findings illustrate the interconnected nature of these benefits and challenges through the Input-Process-Output model. We specifically emphasize that the advantages of BIM in the decision-making process can be significantly affected if project organizations do not adapt the roles and competencies of decision makers to effectively utilize BIM. The use of BIM therefore brings novel decision-making challenges, which are presented in the discussion. • Digital technologies have a strong influence on the decision-making process during the project design. • Building Information Modeling (BIM) triggers positives impacts for decision-makers, but also challenges that should be addressed. • Building Information Modeling (BIM) enables transparency, stakeholders' collaboration, and decision validation. • Building Information Modeling (BIM) requires reviewing stakeholders' role and competencies to ensure BIM use and information comprehension.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".