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Record W4404516380 · doi:10.1016/j.dib.2024.111144

Shear strength dataset of hollow concrete block masonry with different mortar bedding

2024· article· en· W4404516380 on OpenAlexaboutno aff
Milena Mesa-Lavista, Paola Romo-Letechipía, José Álvarez-Pérez, Ricardo González-Alcorta, Jorge H. Chávez-Gómez, G Fajardo-San Miguel

Bibliographic record

VenueData in Brief · 2024
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
FundersUniversidad Autónoma de Nuevo LeónConsejo Nacional de Ciencia y Tecnología
KeywordsMasonryMortarBeddingShear (geology)Geotechnical engineeringMaterials scienceComposite materialGeologyBlock (permutation group theory)Structural engineeringEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

Masonry is a construction material composed of units (blocks or bricks) joined with mortar. It is one of the most widely used materials in construction resisting both vertical and horizontal forces in single and multi-family housing buildings. A correct union between the units and the mortar (interface) is essential, as is determining the resistance from the applied loads. There is a divergence in how the mortar is bedded in the construction of masonry walls. In some countries, such as Canada and Australia, regulations require that the mortar be placed in face shell bedding when hollow blocks are used. However, in countries like Mexico, regulations establish that it be placed in the net area, and construction practices often differ. Much research has been conducted to study the compressive behavior of mortar bedding in masonry of hollow concrete blocks. However, fewer studies have focused on shear behavior. This paper presents the dataset of experimental laboratory tests on wallettes built with hollow concrete blocks. Two methods of mortar bedding were employed: over the net area and the lateral face. The values obtained can be used to compare the shear strength in hollow concrete block masonry and the shear failure. Additionally, they can be useful for calibrating numerical models.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.591

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 head0.015
GPT teacher head0.238
Teacher spread0.223 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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