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Record W4234420714 · doi:10.22215/etd/2021-14328

The History, Mechanics, and Strength of Stone Buttresses in Canada

2021· dissertation· en· W4234420714 on OpenAlexafffundabout
Jamie Marrs

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsCarleton University
FundersFederal Emergency Management AgencyNatural Sciences and Engineering Research Council of CanadaUniversity of Cambridge
KeywordsButtressMasonryEngineeringPlane (geometry)Structural engineeringGeotechnical engineeringForensic engineeringGeologyMathematicsGeometry

Abstract

fetched live from OpenAlex

Stone buildings found across Canada were constructed in a different era according to different engineering methodologies than new buildings. These buildings are important representations of the history of the country and should be preserved for future generations. One of the major concerns with these structures is the out-of-plane strength of the walls under earthquake loads. Many of these buildings, notably churches, include buttresses which were originally included to improve the out-of-plane strength of the walls. Current Codes and Standards in Canada do not provide guidance for engineers to assess the out-of-plane strength of walls with buttresses. A survey of churches with buttresses in Ottawa was conducted, acquiring the different sizes and dimensions of buttresses in the downtown core. An Applied Element Method (AEM) software was then used to recreate existing experimental data on the out-of-plane and in-plane strength of stone masonry walls, and the model was modified to analyze the behaviour of buttresses. The results from the modelling program are related back to historic and modern analysis methods obtained from a thorough literature review. i This thesis would not have been possible without the incredible support that I have received.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.004
GPT teacher head0.169
Teacher spread0.165 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations3
Published2021
Admission routes3
Has abstractyes

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