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Record W3040617233 · doi:10.1051/e3sconf/202017208003

Two-dimensional hygrothermal modelling of masonry walls accounting for imperfections at the masonry joint

2020· article· en· W3040617233 on OpenAlexaff
Michael Gutland, Scott Bucking, Mario Santana Quintero

Bibliographic record

VenueE3S Web of Conferences · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsCarleton University
Fundersnot available
KeywordsMasonryMortarMaterials scienceFracture (geology)Settlement (finance)MoistureStructural engineeringMaterial propertiesJoint (building)Geotechnical engineeringComposite materialGeologyEngineeringComputer science

Abstract

fetched live from OpenAlex

Hygrothermal models are important tool for assessing the risk of moisture-related decay mechanisms such as freeze-thaw in historic masonry structures. There are several sources of uncertainty when modelling masonry, related to material properties, boundary conditions, quality of construction and twodimensional interactions between mortar and unit. This paper examines one potential source of uncertainty; the imperfect nature of mortar joints. This interface may feature hairline cracks or imperfect bonds which can be modelled as a fracture. This will alter the rate of liquid transport into and out of the wall and impede the liquid transport between mortar and masonry unit. This means that the “effective” liquid transport of the wall system will be different then if measured properties of the bulk material were modelled. A detailed methodology for modelling the interface as a fracture is presented including material property definition. Two-dimensional DELPHIN models of masonry walls were created to simulate this interaction with varying levels of fracture widths (apertures). A series of hygrothermal simulations were performed to demonstrate change in moisture profile from the baseline condition. A significant increase in moisture absorption was found. This was dependent on aperture size, material and the relative size of the masonry modelled.

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 categoriesInsufficient payload (model declined to judge)
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 score0.354
Threshold uncertainty score0.999

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)0.0020.000

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 head0.056
GPT teacher head0.227
Teacher spread0.171 · 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.

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
Published2020
Admission routes1
Has abstractyes

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