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Record W3011541436 · doi:10.1139/cjce-2019-0281

Stability coefficient of interlocking compressed earth block masonry under axial compression

2020· article· en· W3011541436 on OpenAlexvenueno aff
Tianya Wang, Yihong Wang, Jianxiong Zhang, Dan Hong Shi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMasonryInterlockingBrittlenessCompressive strengthMaterials scienceCompression (physics)Structural engineeringComposite materialGeotechnical engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

To investigate the effects of the height–thickness ratio (β) on the mechanical properties and stability coefficients ([Formula: see text]) of interlocking compressed earth block (ICEB) masonry members under axial compression, four groups of specimens with different β of 3.75, 6.75, 11.25, and 14.25 were tested, thereby assessing their stress process, failure mode, compressive strength, and in- and out-of-plane deformations. All the specimens underwent brittle failure under axial compression: the compressive strength was found to decrease in a range from 5.6% to 43% with increasing β, whereas the initial stacking defects and the in- and out-of-plane deformations increased. The specimens became less stable, and we noticed that the overall damage was caused by strength failure and not instability failures. Because the stability coefficient of ICEB-based masonry components cannot be calculated as those of more conventional brickwork, we combined our results with well-established masonry design guidelines and derived an interlocking improvement coefficient. Finally, we proposed a formula for calculating the axial compression stability coefficient of ICEB masonry that can provide a useful reference for subsequent research on ICEB walls.

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 score0.143
Threshold uncertainty score0.767

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 head0.021
GPT teacher head0.188
Teacher spread0.168 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicHygrothermal properties of building materialsFrench-language works237,207