Bibliographic record
Abstract
Abstract BIMing Construction Engineering Curricula Don Chen1 and John Hildreth21 Assistant Professor, Department of Engineering Technology and Construction Management,University of North Carolina at Charlotte, Charlotte, NC; Phone (704) 867-6299; Fax (704) 867-6577; email: dchen9@uncc.edu2 Assistant Professor, Department of Engineering Technology and Construction Management,University of North Carolina at Charlotte, Charlotte, NC; Phone (704) 867-6166; Fax (704) 867-6577; email: john.hildreth@uncc.eduAbstractBuilding Information Modeling (BIM) has been used by various construction engineering(ConE) programs to fulfill the Body of Knowledge (BOK) requirements, such as cost estimating,construction scheduling and control, project administration, and contract documents. Currently anumber of BIM software packages are available to ConE educators. However, guidance to selectan appropriate BIM software and an understanding of how this software can be used to instructaforementioned requirements is minimal to nonexistent. This paper seeks to address thesechallenges by developing a BIM model of a case study building using one of the most popularBIM solutions, Autodesk Revit products and Navisworks, and a commonly used schedulingsoftware,. 4D simulations and clash detection of the BIM model are performed. And theprocedures of achieving the 5th dimension of the BIM model, cost estimating, are recommended.This paper outlines strengths and limitations of software packages used in this study and anotherBIM solution, Vico Virtual Construction Suite, and presents a suggested work flow for a futureBIM course. The findings of this paper have tremendous potential to directly benefit ConEeducators by providing a template to integrating BIM into an existing course or implementing astandalone BIM course within construction engineering curricula throughout the country.KeywordsBuilding Information Modeling (BIM); construction engineering (ConE); Body of Knowledge(BOK); Autodesk Revit; Navisworks; Vico
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".