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Record W1993774617 · doi:10.1680/ehah.14.00009

Impact of climate on multi-wythe stone masonry walls

2015· article· en· W1993774617 on OpenAlexafffundabout
Andrea C. Isfeld, Nigel G. Shrive

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Engineering History and Heritage · 2015
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsMasonryMortarGeotechnical engineeringLime mortarGeologyDeformation (meteorology)GroutStructural engineeringEngineering

Abstract

fetched live from OpenAlex

The Hudson Bay Trading Company constructed the Prince of Wales Fort in the early eighteenth century with the goal of securing the fur trade in northern Canada. As a result of the fort's northern latitude the walls remained partially frozen throughout much of each year until recently. Warming in the climate has raised the average yearly temperature so freeze–thaw cycles have caused a breakdown of the mortar within the stone masonry, enabling washout from melting snow and rain. This has led to lateral deformation in some sections of the walls and collapse in others. Two-dimensional finite-element models have been formulated to represent the in situ conditions of a damaged wall section with varying strength mortar and bond conditions. The models were created in Abaqus and focus on the case of self-weight. Micro-modelling techniques were employed to model the stones and grout individually. Results indicate a significant reduction in either the strength or bonding capacity of the mortar will lead to instability in the wall sections studied. The research identifies a new general mechanism of failure for multi-wythe masonry walls and their susceptibility to environmental conditions, of which practising engineers should be aware when assessing heritage masonry structures.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.798

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 head0.014
GPT teacher head0.204
Teacher spread0.190 · 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 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

Citations3
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Engineering History and HeritageSame topicMasonry and Concrete Structural AnalysisFrench-language works237,207