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Record W4413801151 · doi:10.1016/j.jobe.2025.113910

Double-punch compressive resistance of Eastern Canada's low-strength mortars

2025· article· en· W4413801151 on OpenAlexafffundabout
Romaric Léo Esteban Desbrousses, L. A. Brickman, Ronaldo S. Gallardo, Lucy Davis, Daniele Malomo

Bibliographic record

VenueJournal of Building Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCompressive strengthMortarMaterials scienceComposite materialMetallurgy

Abstract

fetched live from OpenAlex

This study provides the first experimental characterization of low-strength mortars sampled from pre-code unreinforced masonry (URM) buildings in Eastern Canada using the double punch test (DPT) and develops a unique dataset of compressive strengths of existing mortars found in the region. The structural assessment of existing unreinforced masonry (URM) structures requires the accurate in-situ characterization of the masonry components’ mechanical properties. This proves challenging in the case of mortar due to the difficulties in extracting specimens from existing buildings that meet the dimensional requirements of standard testing techniques such as CSA A179, ASTM C109, and EN 1015-11. As such, alternative testing methods, such as the double punch test, have been developed to estimate the mortar compressive strength using specimens sampled from existing mortar joints. This paper presents an experimental campaign that uses the DPT to assess the compressive strength of six low-strength mortars, five sampled from existing URM buildings located in Montreal, QC (Canada), and one mixed and cast in a controlled laboratory environment. This study first compares the outcomes of the DPT for the five old mortars by focusing on the relationship between DPT compressive strength and specimen thickness, density, mortar chemical composition, and original joint type. A parametric study is then performed using mortar specimens prepared in a controlled laboratory setting to further delve into the effect of specimen thickness on DPT results and compare mortar compressive strength predictions from the DPT, penetrometer test, and prism compression test. The results indicate that, for the tested old mortars, DPT compressive strength is inversely proportional to specimen thickness and marginally affected by changes in specimen density. The experiments further suggest that mortar specimens sampled from head and bed joints yield similar DPT compressive strengths. The parametric study then indicates that the DPT tends to predict higher mortar compressive strengths compared to estimates obtained with the penetrometer and prism compression tests. A key contribution of the work presented herein is the development of an experimental dataset that characterizes the DPT compressive strength of some low-strength mortars extracted from existing pre-code URM buildings located in Eastern Canada, thereby providing a first step towards characterizing the mechanical properties of such materials. Another novelty of this study involves comparing the mortar compressive strength estimates obtained with standard experiments (e.g., cube compression test) and alternative experimental methods (e.g., DPT, penetrometer test) for a low-strength laboratory-prepared Type O mortar used locally in masonry restoration works. • DPT is used to estimate the compressive strength of six low-strength mortars, including five mortars sampled from old URM buildings typical of Eastern Canada • Compare DPT results for mortars extracted from URM buildings with different end uses • Evaluate the effect of mortar specimen thickness, density, chemical composition, and original joint type on DPT results • Compare outcomes of the DPT with other test methods such as the penetrometer, prism compression, and cube compression tests

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.825

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 head0.003
GPT teacher head0.194
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 designBench or experimental
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

Citations0
Published2025
Admission routes3
Has abstractyes

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