Critical success factors for adopting building information modelling (BIM) and lean construction practices on construction mega-projects: a Delphi survey
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate critical success factors (CSFs) that enhance integration between building information modelling (BIM) and lean construction (LC) practices on construction mega-projects. BIM and LC have gained momentum in the past decade. Design/methodology/approach The Delphi survey technique was used to gauge opinions of a panel of 16 experts through a two-round Delphi questionnaire survey. Panel responses were scrutinised using inferential and descriptive statistical techniques. Findings In total, 30 CSFs were identified in the literature. The top ranked factor out of 30 that supports LeanBIM synergy was “collaboration in design, construction works and engineering management”. Other top rated CSFs were centric on people, data and technology elements. The research findings are important for project stakeholders, organisations, contractors, engineers and local authorities who implement LC and BIM synergies in construction mega-projects. Originality/value The research findings are important for project stakeholders, organisations, contractors, engineers and local authorities who implement LC and BIM synergies in construction mega-projects. The research recommends further hands-on training to increase the integration of BIM and LC practices in the architecture, engineering and construction industry and to enrich the extant body of knowledge in construction of mega-projects.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".