MétaCan
Menu
Back to cohort
Record W4414132484 · doi:10.1063/5.0286406

Experimental and numerical characterization of a circular unbonded fiber-reinforced elastomeric isolator with high-damping

2025· article· en· W4414132484 on OpenAlexaff
Gaetano Pianese, Niel C. Van Engelen, Gabriele Milani

Bibliographic record

VenueAIP conference proceedings · 2025
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsIsolatorElastomerStiffnessShear (geology)Computer simulationNumerical modelsRobustness (evolution)Characterization (materials science)

Abstract

fetched live from OpenAlex

Fiber Reinforced Elastomeric isolators (FREis) represent a novel category of elastomeric seismic isolators. in contrast to conventional Steel-Reinforced Elastomeric isolators (SREis), FREis incorporate slender layers of fibers instead of steel laminates to provide vertical reinforcement. These isolators can be utilized in various configurations, including bonded, unbonded, and partially bonded setups. in the unbonded configuration, the isolator is positioned between the superstructure and the foundation without bonding or fastening. The shear load transfer relies on the friction generated between the isolator and the structure. This application is characterized by rollover deformation, which reduces horizontal stiffness and enhances the damping capacity of the isolator compared to its bonded counterpart. This study proposes a combined numerical and experimental approach to characterize the lateral behavior of FREis. A preliminary numerical model, based on simple material tests, is presented to predict the horizontal characteristics. Subsequently, a prototype is fabricated and subjected to experimental tests, with the results compared to the numerical predictions. The outcomes of this research underscore the robustness of the numerical model, which can be employed both for predicting lateral behavior and anticipating future structural applications.

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 score0.523
Threshold uncertainty score0.490

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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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 routes1
Has abstractyes

Explore more

Same venueAIP conference proceedingsSame topicVibration Control and Rheological FluidsFrench-language works237,207