Minimizing Flooring Strip Weight: A Shape Optimization Approach
Bibliographic record
Abstract
In North America, flooring strips are manufactured with grooves at the back. There are various reasons for these grooves but, historically, they were considered a strategy to reduce weight and transportation costs as well as improving dimensional stability. As no data are available to assess best practices in terms of performance, we have investigated methods to reduce flooring strip weight. One way to achieve this is to adjust the number and shape of grooves. Using warp as a comparison tool, we were able to analyze the merits of a finite number of designs. With this approach, however, we could not guarantee that the result was the most favourable. The search for a solution led to design optimization, i.e.: minimizing weight by acting upon a part of the strip’s shape, taking into account its warp resistance or stiffness. This paper describes an optimization strategy adapted to the calculation of the optimal design subjected to arbitrary mechanical and geometrical conditions (including the thickness of the wear layer). This approach is not limited to flooring strips, and it can be used in any situation where a linear hygromechanical model is relevant. This strategy involves two steps: global optimization with respect to admissible variations of the shape (or design) followed by a post-processing phase that takes into account various other mechanical and possibly geometrical conditions imposed on the strip.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".