Simulating Wood-framing Wall Panels Production with Timed Coloured Petri Nets
Bibliographic record
Abstract
Simulating Wood-framing Wall Panels Production with Timed Coloured Petri Nets Fabiano Correa Pages 1026-1033 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: The integration of design and construction processes remains, after decades of dedicated research, a great challenge. Even considering the specific context of pre-fabrication and modularization, it was just in recent years, with increasing adoption of Building Information Modeling (BIM) processes, that the challenge, albeit in a virtual environment, begins to be really addressed. With the advent of the Digitization phenomena in Construction, and the advances in Machine Learning techniques to cope with uncertainties of different natures in modelling real processes, it seems that the use of computational tools to simulate off-site production should be reconsidered. In this article, it is adopted an approach in viewing BIM as in a development stage to become an implementation of Product Lifecycle Management (PLM) for Construction. Towards this end, it is identified the lack of representation of the entire dynamics of production processes inside BIM models. The proposition of using Petri Nets with stochastic transitions to represent and simulate those processes are presented, altogether with the use of real RFID data, to adjust the model parameters, collected from a case study with a Brazilian company that pre-fabricate wood-framing houses. The probability distributions are derived based on the Mixture of Gaussians algorithm, and considers parameters of the design of wall panels so it could be used to extrapolated performance for new designs. Following the presented approach, it is expected that, with more data, the simulation process could be a good feedback to architects in evaluating the impact of its design options in production. Keywords: High-level Petri Nets; Building Information Modeling; Simulation; Wood-framing DOI: https://doi.org/10.22260/ISARC2019/0137 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".