Bibliographic record
Abstract
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.Professors Erochko and Santana-Quintero, you have both been instrumental to the completion of this document and helping me keep my ideas within a reasonable scope while still encouraging that I explore each of the sub-topics that arose over the last two years.I would also like to thank the NSERC CREATE Heritage Engineering program and Carleton University for the financial support that made this journey possible.To all of my co-workers at John G. Cooke & Associates Ltd., thank you for your instruction, for sharing your experiences, and for being understanding of the time required to complete a document of this nature.Without the experience of working with stone buildings, this document could never have come together in a way that is (hopefully) practical as well as informative.To my parents, thank you for constantly showing me that engineering is about logic and common sense as much as mathematics, and that you can always keep learning.And finally to Ross, thank you for always being there and offering the encouragement and support that I needed to finish this process.These past months have been hard, but it has been incredibly valuable to be able to discuss my ideas with you to help shape this thesis.I could not have done it without you.how to assess the strength of these buildings, even though this construction typology forms a significant proportion of our current building stock.For example, based on a 2014 survey, 11.2% of buildings in central Ottawa are unreinforced masonry ((Sabbagh, 2014)).Another major recent shift in structural engineering is the focus on seismic analysis.The field of seismology has experienced significant advancements, including recording of seismic
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".