Measuring the value and cost of BIM use—an empirical lesson learned from Taiwan’s social housing projects
Bibliographic record
Abstract
This paper presents a novel approach for measuring the cost requirements and related values for building information modeling (BIM) adoption using the adaptive analytic hierarchy process approach. The proposed approach considers the importance of each BIM use item in relation to its cost, allowing for prioritization of BIM uses based on their relative value while considering prerequisite relationships and budgetary constraints. The effectiveness of the approach was demonstrated through empirical validation involving a survey of 50 construction industry professionals, and two BIM use prioritization methods (project objective- oriented and value- oriented) were recommended for decision-making under budget constraints. The lessons learned from the empirical case study include the importance of Government’s role and the industrial standards in evaluating cost-benefit and promoting for BIM adoption, the different contributions of BIM uses across project phases, and the diverse perceptions of BIM cost requirements among different participants. These lessons are invaluable for countries planning BIM implementation. This paper also demonstrates the development and empirical validation of a novel approach for BIM use adoption under limited budgets, which can assist decision-makers in selecting appropriate BIM use items and estimating their associated costs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".