BIM-based Takt-Time Planning and Takt Control: Requirements for Digital Construction Process Management
Bibliographic record
Abstract
BIM-based Takt-Time Planning and Takt Control: Requirements for Digital Construction Process Management Juergen Melzner Pages 50-56 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Continuous and robust process planning is contrary to the different goals of project participants in the construction business. The aim of holistic building process management must be to optimize the overall process by streamlining individual processes. Lean management methods are increasingly being used to harmonize building processes. For this purpose, especially the method of takt time planning and takt control is appropriate. Building information modeling (BIM) is another promising way to promote a collaborative planning and construction process. BIM is generally understood as a virtual 3D model of a project with additional information. In order to be able to use the synergies of the two methods, the requirements, framework conditions, and goals of the two methods must be coordinated so that the added value of information for process planning can be used. The parallel application of lean construction methods and BIM can create added value that leads to productivity gains. In established BIM applications, the product (e.g., building) is planned as optimally as possible. However, the production process is not sufficiently considered. This is where lean construction methods are used to optimize the process. This article describes synergies through the combination of both methods and defines the requirements for a new BIM use case, "takt time planning and takt control." The presented concept is prototypically tested on a hotel tower project and the benefits and requirements are discussed. Keywords: Lean Construction; Process planning; Construction Management; Building Information Modeling DOI: https://doi.org/10.22260/ISARC2019/0007 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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".