Automatic Generation of the Vertical Transportation Demands During the Construction of High-Rise Buildings Using BIM
Bibliographic record
Abstract
Automatic Generation of the Vertical Transportation Demands During the Construction of High-Rise Buildings Using BIM Keyi Wu, Borja Garcia de Soto, Bryan T. Adey and Feilian Zhang Pages 99-106 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: The explosion of high-rise building projects has increased the awareness on the importance of the planning and management of vertical transportation systems (i.e., tower cranes, construction elevators and concrete pumps). Although researchers have made beneficial efforts in several aspects of vertical transportation systems (e.g., optimal design capacities and layouts), the estimation of demands on vertical transportation systems (i.e., the quantity of construction resources associated with location, trip date and vertical transportation mode) has not been fully integrated. Currently, this process is still done manually. Building information modeling (BIM) provides the possibility to automate this process, decreasing the time it takes to gather that information and reducing errors associated with manual collection and quantification. This paper proposes a BIM-based framework to generate the vertical transportation demands during the construction of high-rise buildings. It consists of six parts: (1) determine the vertical transportation information of building materials, (2) generate the vertical transportation information of temporary construction materials, (3) link the project schedule with construction materials, (4) generate the vertical transportation information of construction workers, (5) determine the vertical transportation mode for construction materials, and (6) generate the vertical transportation demands. A prototype tool, in the form of an add-in using Revit API, has been developed to demonstrate the functionality of the proposed framework through testing the BIM model of a 36-story high-rise building. The findings show that the framework allows to exploit BIM to generate the information needed to determine the vertical transportation demands quickly and effortlessly. Keywords: BIM; Vertical transportation demands; Vertical transportation systems; High-rise buildings DOI: https://doi.org/10.22260/ISARC2019/0014 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".