Model validation and 2-D hygrothermal simulations of wetting and drying behavior of cross-laminated timber
Bibliographic record
Abstract
Cross-laminated timber (CLT) is one of the most important mass timber materials that are commonly used in mid-rise or even high-rise timber buildings. However, exposure to moisture during construction may increase the moisture damage risks, and impact the durability performance of CLT buildings. To investigate potential solutions for avoiding wetting of CLT components during construction, CLT specimens having different moisture protection measures were tested in the damp and mild wintertime climate in Vancouver. This follow-up work focuses on two-dimensional (2-D) hygrothermal modeling of the wetting and drying behavior of bare CLT (without any protection) and the validation with measurements from the field exposure test, emphasizing the influence of material properties. The hygrothermal models are firstly calibrated for two CLT specimens positioned horizontally, with and without a butt joint, by using material properties from different laboratory tests, and assuming different rain penetration paths. The calibrated models are then applied to simulate CLT specimens positioned vertically, which have end grain directly exposed to rain or damp concrete in the test. The work reveals that the moisture storage function above RH 95%, which includes the saturation water content reported in different literature, has a significant influence on the hygrothermal simulation results; meanwhile, assigning different water absorption coefficients for the transverse and longitudinal directions of wood significantly improves the accuracy of the hygrothermal model created for simulating rainwater penetration into the CLT panel. This paper provides a recommendation on how to properly model the CLT panels exposed to rainwater, which often occurs during construction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".