Assessment of the Effects of Extreme Heat Events on Buildings
Bibliographic record
Abstract
Due to urbanization and global warming, extreme heat events, e.g., heat waves, in the urban area tend to occur much more intensively and frequently, imposing a great threat to the health and safety of urban dwellers. The warming temperature also leads to a deteriorating of the indoor thermal conditions. Studies on indoor overheating should be conducted to figure out the interactions between the outdoor environment and indoor conditions and estimate possible approaches to optimize the design and operation of the building system adapting to the changing climate. Considering the building and construction assets are typically designed for a long period over several decades or even centuries. The resiliency of the buildings to extreme conditions should be reconsidered under the future climate conditions, therefore the future weather inputs should be critical for the building overheating study, and any possible mitigation intervenes should be re-evaluated under different future scenarios to evaluate the robustness of the decision and detect the potential risks. The study includes a procedure to select buildings from Montreal city for the field monitoring study, followed by the outcome of building information surveys and site visits. After an overall evaluation of these buildings, 6 school buildings, 6 hospital buildings and 3 residential social housings have been selected for further studies. The measured data are used for the overheating assessment of these buildings and the calibration of these real building models. The measured data exhibited strong evidence of overheating in existing building stocks in Montreal, showing the necessity for further investigation to mitigate the overheating. To consider the spatial distribution in urban climate, the study developed a high-resolution regional climate model for the Montreal and Ottawa region and elaborated the importance of preserving the urban effect in weather files for building studies. The generated climate dataset can be used as the input of EnergyPlus building simulations to evaluate the spatial-temporal pattern of indoor overheating. The validated climate model can also be extended for the future projection of urban scale overheating studies. In the future, overheating mitigation strategies can be applied to the baseline model to evaluate their effectiveness on both historical and future climate in the long run with the proposed workflow and the climate dataset generated by this study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".