Design of a structural insulating panel based on wood-based corrugated panels as an alternative to light-frame construction
Bibliographic record
Abstract
The performance of building envelopes is one of the cores of environmental issues related to the construction sector. Lightweight wood framing, widely used in North America, appears to be a solution to be favored in this context. However, since structural thermal bridges are now considered in the thermal resistance of an opaque wall by the Canadian National Building Code, its thermal performance remains limited if it is to be maintained. Structural insulated panels (SIPs) are becoming increasingly popular in the scientific literature on construction. These systems have advantages over conventional systems, such as light-frame construction. This study focuses on the design of new envelope compositions based on wood-based corrugated panels. The design was carried out by reverse engineering conventional light-frame envelope systems. A functional specification was established based on the performance of traditional envelopes. These performances were evaluated according to thermal resistance, mold growth index, assembly time, environmental impact, cost, dimensions, etc. To calculate the mold growth index and thermal resistance, a model was built according to ASHRAE 160. It was run over a four-year period on COMSOL software. The results showed that one of the prototypes designed outperformed lightweight wood framing in terms of the specifications. • A design approach based on reverse engineering and specifications enabled us to design high-performance prototypes. • Mold growth indices for the compositions studied were calculated using Four-year hygrothermal simulations on COMSOL. • SIP-type assemblies integrating corrugated panels outperformed the currently recommended assembly.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".