Development of an Inventory Modelling Framework for Seismic Risk Assessment of Residential Buildings in Eastern Canada
Bibliographic record
Abstract
ABSTRACT Seismic risk assessment for residential buildings is a priority in Eastern Canada, given its densely populated cities and history of earthquake activity. A crucial component of this assessment is the development of an accurate and practical inventory model, which relies on comprehensive investigations and the collection of reliable data on residential buildings. A simple yet reliable inventory framework is essential to streamline the process of building inventory while reducing costs and time. Moreover, there is a need for more refined and standardized classifications of the structural systems of residential buildings. This study proposes a new inventory modelling framework for residential buildings, applied to Montreal as a case study, with a focus on the number of residential units. The two main objectives of this study are: (1) to conduct a historical review of residential construction practices in the city, defining common materials and structural systems; and (2) to determine their distribution across administrative areas, including both independent municipalities and boroughs within the City of Montreal. To achieve these objectives, previous studies and various pertinent resources were evaluated to trace the evolution of residential construction, and two open‐access databases were employed and integrated to derive results. The analysis covers over 900,000 residential units, revealing that approximately 30% and 22% are associated with buildings constructed using wood light frames and concrete shear walls, respectively, while 48% correspond to buildings with mixed wood–masonry structural systems as well as masonry buildings. This inventory model offers practical insights into the distribution of residential units by structural systems, improving future simulations to estimate uninhabitable unit rates, population displacement, and shelter needs, which will support and strengthen community resilience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".