Involving knowledge of construction and facilities management in design through the BIM approach
Bibliographic record
Abstract
The construction industry has increasingly realised the importance of knowledge.Accordingly, various strategies and tools have been applied over the years to support knowledge management (KM).In particular, building information modelling (BIM) is a technology that has recently emerged in the construction industry.BIM is an object-oriented and parametric-based tool with the features of digital visualisation, life cycle simulation, coordination and collaborative environment.Consequently, many studies have been conducted to explore these four aspects.However, existing studies on BIM-based management mainly focus on the information level.By contrast, only a few studies have explored KM under the BIM environment.Therefore, this study explores the potential and expectations of BIM-based KM for the early application of knowledge of construction and facilities management (FM) into the design stage.A total of 30 semi-structured interviews are conducted to collect qualitative information from the AEC industry.The existing KM practice is explored based on the analysis of the collected qualitative information.Thereafter, a discussion is presented on how the BIM-based KM can be used to mitigate the current KM challenges.Lastly, this study presents the expectations on BIMbased KM for involving the knowledge of construction and FM into the design phase.Overall, this study provides new insights into the transformation of research focus from BIM-based information management to BIM-based KM.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".