Guideline on assessing the effects of climate change on the resilience to moisture-related degradation of building envelopes
Bibliographic record
Abstract
Climate change is bringing more frequent and severe weather events, along with an overall trend toward more intensive environmental loads acting on buildings, such as higher temperatures and wind-driven rain. The increase in environmental loads poses a hazardous trend for building envelope systems, whose function is to separate indoor and outdoor environments, because these higher loads are likely to introduce a greater risk of moisture-related deterioration of building envelope components over their service life, as well as a higher risk of indoor overheating during summertime heat waves. As defined by the IPCC, climate resilience is the “capacity of social, economic and ecosystems to cope with a hazardous event or trend or disturbance.” For building envelope systems, design activities need to take this hazardous trend—the increase in environmental loads—into consideration in order to help ensure climate resilience against: (1) moisture-related deterioration of building envelope components; and (2) heat-wave-induced indoor overheating. This document focuses on the first aspect of climate resilience of building envelopes, which essentially refers to the moisture performance of building envelopes subjected to changing climates. It provides guidance on the use of a stochastic approach to assess whether building envelope systems are sufficiently resilient to withstand moisture-related consequences resulting from the effects of climate change. Hygrothermal simulation is generally used to assess the heat and moisture transport behaviours in building envelope systems and to predict moisture-related deterioration of building envelope components. Lacasse et al. (2018) developed a guideline on the design for durability of building envelopes using hygrothermal models to provide results from which to infer the durability performance of building envelope systems. In that guideline, high-level guidance was provided on the procedure for durability performance evaluation, either for short-term comparative studies among different building envelope systems under different climate change scenarios or for long-term service life assessment of a specific building envelope system under a specific climatological period. This guideline provides more detailed information on short-term comparative studies while considering uncertainties in input parameters, such as future climate data, boundary conditions, and building material properties. These uncertainties arise from the process of preparing the input information and can be categorized as systematic uncertainty and random uncertainty. For example, future climate data can be generated from different meteorological models, and the uncertainty caused by differences among these models can be considered systematic uncertainty. On the other hand, for a given meteorological model, there is always unknown information required to feed the model, such as the initial conditions for meteorological modelling, and this uncertainty can be considered random uncertainty. In addition, with the progression of climate change and uncertain human responses to climate change, the input information is subject to variation. For example, boundary conditions are influenced by microclimate conditions, which are highly affected by changes in local landscapes. Building material components are also subject to aging due to more frequent extreme weather events and more intensive environmental loads, and material property values may deviate from originally expected values as a result of the aging process. All of these uncertainties influence the reliability of using deterministic hygrothermal simulations to assess the climate resilience of building envelopes. Therefore, a stochastic approach should be used to account for all uncertainties in order to obtain probabilistic simulation results that can be used to assess the risk of moisture-related deterioration and to develop effective design or mitigation solutions that improve climate resilience against moisture deterioration. The information provided in this document describes the procedure used for hygrothermal performance analysis and probabilistic risk assessment of moisture-related deterioration in building envelope systems using stochastic simulation. It is intended for use by expert practitioners who have knowledge of hygrothermal simulation and require hygrothermal performance results to assess climate-resilient building envelope design or retrofit while considering uncertainties in input parameters. Given that stochastic simulations require extensive computational resources and advanced programming skills, which are not widely available in the building design or simulation industry, this document also provides a case study comparing stochastic and deterministic simulation results to help practitioners understand the confidence level associated with using a deterministic approach to assess the climate resilience of building envelopes. The results of this study indicate that stochastic simulation incorporates much more information than deterministic simulation and provides a more robust assessment of the risk of moisture-related degradation in wall assemblies. A sensitivity analysis showed that reducing wind-driven rain and increasing cladding ventilation rates are two important design strategies for mitigating the risk of moisture-related degradation in the envelope, as these two strategies have relatively high sensitivity indices and can significantly reduce the mould growth index, even in the presence of other uncertainties.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.021 |
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".