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Record W2084223264 · doi:10.1139/l01-094

Damages to residential buildings related to pyritic rockfills: field results of an investigation on the south shore of Montreal, Quebec, Canada

2002· article· en· W2084223264 on OpenAlexvenueaboutno aff
Gérard Ballivy, Patrice Rivard, Caroline Pépin, Marc Tanguay, Alexandre Dion

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsSubbaseGeologySlabGeotechnical engineeringShorePyriteBasementMining engineeringSedimentary rockLeveeEnvironmental scienceGeochemistryEngineeringCivil engineeringOceanography

Abstract

fetched live from OpenAlex

Numerous cases of garage and basement concrete slab distress at various levels of heaving and deterioration were noticed early after construction in the Montreal south-shore area (Canada). The use of clayey sedimentary rock containing pyrite as aggregates for granular subbase is responsible for most of the observed distress. The heaving of the slabs is related to the oxidation process of the pyrite, followed by sulphate crystallization causing the swelling of the compacted granular subbase. A first inspection survey was carried out in the summer of 1999 in an attempt to assess the extent and magnitude of the damage caused by pyritic rockfills. More than 200 houses showing distress associated with pyrite were investigated in three localities. Damage of the garage floor slab seems to occur earlier after construction and to be more severe likely due to the subbase thickness and to the lower quality of the rockfill. Five houses at various stages of reaction process were monitored. The results suggest that the rockfill swelling is quite fast and continuous. The heaving rate of some garage slabs might be as high as 1.2 mm/month.Key words: pyrite, shales, swelling rockfill, oxidation, sulphate attack, damage.

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)0.001
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 score0.562
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.168
Teacher spread0.160 · 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

Citations14
Published2002
Admission routes2
Has abstractyes

Explore more

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