Towards adopting 4D BIM in construction management curriculums: A teaching map
Bibliographic record
Abstract
Construction planning and scheduling are vital to ensure delivering the project according to the agreed completion date. With the increasing in adopting technology in construction, the construction planning and scheduling process has been improved. One of these technologies is the 4D Building Information Modelling (BIM), which can be employed to improve construction planning and scheduling. The development of 4D models facilitates the various participants of a building project from architects, designers, contractors to the clients to envision the total duration of a series of events and also displays the progress of the overall on-going construction activities through the lifetime of the project. 4D BIM enables planners to attach the design elements to the corresponding activities, therefore, a simulation of construction sequences can be created. This chapter was designed for educators and students to provide them with adequate knowledge to teach and study 4D BIM, therefore, an introduction about planning and scheduling in construction was presented, followed by, an overview of BIM, then, the process and implementation of 4D BIM were presented in different sections. Finally, we provided educators with a teaching map to enable them to build their curriculums.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".