Cost management-based BIM: skills, implementation and teaching map
Bibliographic record
Abstract
It is widely known that the emergence of Building Information Modelling (BIM) is significantly affecting the cost management process and the role of quantity surveying professionals in the construction industry. The utilization of BIM is moving towards transforming how information is managed by and for quantity surveying professionals, especially due to the transition from 2D drawings. This chapter starts with describing the conventional cost management processes and highlighting the shortcomings of using it. Subsequently, the role of BIM to tackle those challenges through providing an autoamted quantification feature and integarting the cost estimation into the desisgn process. The integration of 4D and 5D BIM is discussed in this chapter to provide a siginfcant understanding how BIM enahanced the process of develop a budget of construction projects. This chapter also proposes an effective process to teach 5D BIM at both the undergraduate and postgraduate levels. The process is expected to enable a deep learning for students to absorb the required knowledge before starting their careers in the construction industry.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".