Sensitivity Analysis of Hygrothermal Performance of Cross-Laminated Timber Wall Assemblies
Bibliographic record
Abstract
Cross-laminated timber (CLT) panels are increasingly being used in building enclosures due to their good structural and fire safety performance. However, prolonged exposure to moisture during construction and in service are durability concerns for most wood products, including CLT. The wetting and drying behavior of CLT wall assemblies can be studied by hygrothermal simulations in which a deterministic approach is normally used. However, in reality, there are always uncertainties in input parameters—such as material properties, environmental loads, and design variables—that may lead to discrepancies between simulation results and actual performance. The hygrothermal performance of 16 CLT wall assemblies with various design configurations was tested in a building envelope test facility, and discrepancies between simulations and measurements were observed. This paper further investigates the discrepancies between simulations and measurements of a CLT wall assembly with two different types of water-resistive barriers (WRBs) that were caused by the uncertainties of input parameters using sensitivity analyses. Simulation results obtained from DELPHIN and WUFI Pro simulation programs are compared with measurements for validation. The influential factors—including material properties, rain loads, and cladding ventilation rates—are studied using a one-factor-at-a-time method under different environmental loads. The examined parameters are assigned with two extreme values based on their uncertainties. The root mean square difference of CLT moisture content between the cases with the two extreme values is calculated to evaluate the importance of each parameter. The simulation results show that the influence of the moisture storage function is more significant than the moisture transport properties (i.e., vapor resistance factor and moisture diffusivity) and that the wall assembly with a vapor-permeable WRB is more sensitive to the variations in the rain deposition factor and cladding ventilation rate than the wall with a non-vapor-permeable WRB.
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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) | 0.001 | 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) | 0.002 | 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 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".