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Record W4414668077 · doi:10.1002/eer2.70016

Development of an Inventory Modelling Framework for Seismic Risk Assessment of Residential Buildings in Eastern Canada

2025· article· en· W4414668077 on OpenAlexaffabout
Maryam Montazeri, Ahmad Abo El Ezz

Bibliographic record

VenueEarthquake Engineering and Resilience · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsMasonryDistribution (mathematics)Process (computing)Seismic riskRisk assessment

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.006
GPT teacher head0.224
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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