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Record W4206026172 · doi:10.22215/etd/2021-14811

Seismic Analysis of Unreinforced Masonry Structures with Plan Irregularities

2021· dissertation· en· W4206026172 on OpenAlexaff
Elyse Hamp

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsUnreinforced masonry buildingStructural engineeringMasonryReusePlan (archaeology)EngineeringTorsion (gastropod)Vulnerability (computing)Computer scienceGeology

Abstract

fetched live from OpenAlex

Unreinforced masonry (URM) is inherently vulnerable to seismic forces due to its minimal ability to resist tensile forces.Typical structural design features in URM buildings such as torsional irregularities and re-entrant corners increase this vulnerability.This thesis seeks to address the seismic vulnerability of URM structures due to plan irregularities and to contribute to the structural engineering knowledge required to lessen the need for new construction by supporting the reuse, rehabilitation, and ongoing maintenance of existing buildings.A comparative analysis based on the results of nonlinear static and incremental dynamic analyses was carried out on numerical models representing URM structures with plan irregularities typical in Ottawa, Canada.The results from these analyses determined that damages to the structure and the probability of collapse of the structure were reduced overall with the minimization of torsion and full lateral support of the re-entrant corners in the modelled structures.iii

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.197
Teacher spread0.192 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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