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Record W2041616891 · doi:10.1680/stbu.14.00011

Novel anchor-jointed precast shear wall: testing and validation

2014· article· en· W2041616891 on OpenAlexaff
Mohamed El Semelawy, Ashraf El Damatty, Ahmed M. Soliman

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Structures and Buildings · 2014
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsPrecast concreteStructural engineeringShear wallShear (geology)Ductility (Earth science)Deformation (meteorology)Connection (principal bundle)Structural loadMaterials scienceEngineeringCreepComposite material

Abstract

fetched live from OpenAlex

Precast concrete shear walls are used to resist lateral loads in low- to medium-rise buildings. This paper introduces an innovated joining technique for these precast walls that will reduce the possibility of concrete damage and capital loss during earthquake events. The proposed technique consists of precast panels joined together and to the base using threaded steel anchor bolts, which in turn makes the construction process easier and faster. An experimental programme is conducted to validate this system and characterise its behaviour under lateral loads. Four reduced-scale wall specimens are tested under monotonic in-plane horizontal load. The lateral strength, ductility and possible modes of failure are used to evaluate the connection performance. The joined precast shear wall exhibits a high non-linear deformation without any signs of panel damage, which can be attributed to the gap opening and steel yielding at the connection plane. This indicates that the steel anchor bolts at the connection can be designed to act as structural ductile fuses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.195
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Structures and BuildingsSame topicSeismic Performance and AnalysisFrench-language works237,207