Using Scan-to-BIM Techniques to Find Optimal Modeling Effort; A Methodology for Adaptive Reuse Projects
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Bibliographic record
Abstract
Using Scan-to-BIM Techniques to Find Optimal Modeling Effort; A Methodology for Adaptive Reuse Projects Mansour Esnaashary Esfahani, Ekin Eray, Steven Chuo, Mohammad Mahdi Sharif and Carl Haas Pages 772-779 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: With increased computing power to render 3D models and affordability of as-built data acquisition technologies, new techniques for enhancing the quality of pre-project planning of adaptive reuse projects can be investigated. The main objective of this research is to present a decision making methodology to select the optimum effort using 3D as-built point clouds to develop a BIM of an existing building. Three value proposition and risk reduction areas are investigated: (1) dimensional, (2) material, and (3) disassembly. To measure the cost and value of developing models with corresponding value propositions, a small case study is conducted. Three different Model Detail Levels (MDL) are defined for adaptive reuse projects, and corresponding models are developed for each of them. The value of each model is considered based on its ability to provide information about dimension, materials, and fixtures within an existing building. The cost of the scan-to-BIM process includes costs of purchasing 3D acquisition device, buying BIM modeling software license, scanning and registration, and developing BIM using scan-to-BIM techniques. Keywords: Adaptive reuse; Value of information; Pre-project planning; Scan-to-BIM; Modeling; Existing buildings DOI: https://doi.org/10.22260/ISARC2019/0104 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it