Development and calibration of the cross-laminated timber Roof Assembly Moisture and mould (CRAMM) risk assessment tool
Bibliographic record
Abstract
Moisture accumulation and limited drying potential in cross-laminated timber (CLT) roof assemblies can compromise long-term durability through cracking, de-lamination, and mould growth – risks that affect both the durability of CLT and adjacent materials and occupant health. This paper introduces the CLT Roof Assembly Moisture and Mould Risk Assessment (CRAMM) Tool, a calibrated, one-dimensional, simulation-based framework design to support decision-making during the design and construction of mass timber buildings. Implemented in this study using WUFI® Pro and the WUFI Bio and VTT plug-ins and calibrated with in-situ data from a mass timber building in Toronto, Canada, the CRAMM Tool evaluates the effectiveness of both proactive design investment strategies and reactive moisture control and mitigation strategies under observed or predicted exposure conditions. By simulating the hygrothermal response and mould risk of CLT roof assemblies, the tool provides an accessible, scalable risk assessment that enables designers, contractors, and building envelope professionals to better manage the impacts of high-moisture events such as extreme weather, roof ponding, roof leaks, or trapped construction moisture. • Development and validation of novel CRAMM Tool to assess CLT roof moisture control strategies • Calibrated CRAMM Tool using field data from mass timber building during construction • Application CRAMM Tool comparing findings of the effectiveness of moisture control and mitigation strategies • During construction, heat in tented CLT roofs during construction dried CLT faster than ventilation alone • During service, modest ventilation above CLT in service delayed mould onset; higher rates gave less benefit
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".