Learning from Previous BIM-Based Modular Construction Cases: Qualitative Comparative Analysis Approach
Bibliographic record
Abstract
Successful implementation of Building Information Model (BIM) -based modular construction projects is not always guaranteed in different regions and their associated contexts, because success depends heavily on combinations of multiple conditions, including technological, political, social and cultural, and economic ones. Such difference in conditions often hinders a successful modular construction company in a region from continuing its success in other regions; however, understanding the complex causality between the conditions and the success in implementation from previous BIM-based modular construction cases is very challenging because (1) each case omits some conditions and focuses too much on others, which makes the comparison difficult, and (2) cases are insufficient in number for dealing with various conditions, i.e., a small-N or intermediate-N situation. To address this problem, based on the review of previous case studies and modular construction theories, this paper classifies and defines nine condition variables that can be utilized in developing and analyzing BIM-based modular construction cases more comprehensively and systematically. This paper then discusses how the qualitative comparative analysis (QCA) approach can be used to find sufficient and necessary combinations of conditions for successful BIM-based modular construction projects. Upon successful completion, the QCA approach will contribute more structured and generalized explanations of success and failure in BIM-based modular construction to the industrialized construction theory.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".