Modelling and analysis of two production solutions for precast concrete elements: a petri-nets approach
Bibliographic record
Abstract
The adoption of precast concrete construction has been increasingly emphasised globally, due to its advancements in productivity, waste management, cost control, quality and safety. Meanwhile, the production solutions differ greatly in the off-site plants in terms of traditional stationary production and modern circulation production with different levels of automation. Practical cases show that improper selection or upgrade of production solution could lead to resource slack or even business failure, and there is limited knowledge to quantitively support the decision making. Furthermore, existing works seldom dig deep into the fundamental setup of the production plant, and reckon without the stochasticity and variability of production operation. Therefore, this paper aims to develop an analytical model to facilitate the understanding of the production solutions of precast concrete elements, which should further support the decision making in factory planning or upgrade towards increased level of automation. Stochastic Petri-nets approach has been applied, with stochastic features embraced, to graphically modelled stationary and circulation solutions. Simulation and comparative performance analysis was conducted with a numerical case study. Results demonstrate that stationary solution can achieve even higher outputs during production period provided that enough resources are given, whilst circulation solution has a more flexible and stable production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".