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Record W2805323998 · doi:10.1002/cepa.784

Comparison of free‐field and foundation input motions from experimentally tested built environments

2018· article· en· W2805323998 on OpenAlexaboutno aff
Ivan Kraus, Adriana Cerovečki

Bibliographic record

Venuece/papers · 2018
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFree fieldFoundation (evidence)EurocodeEvent (particle physics)Field (mathematics)Motion (physics)Ground motionAccelerationStructural engineeringGeologyIntensity (physics)SeismologyGeotechnical engineeringEngineeringComputer scienceAcousticsMathematicsPhysicsGeographyOpticsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Eurocode allows recorded acceleration time histories for representations of seismic action for design of structures. However, it does not define whether the ground motion time histories should be obtained from instruments mounted on foundations, below foundations or placed in free field. Recent laboratory experiments showed that foundation input motion may be of higher intensity and with different frequency content when compared to the motion recorded in free‐field during same earthquake event. However, results obtained during a real earthquake events showed that input motions at the foundation of a structure are of similar intensity when compared to motions recorded in free‐field, not far from the structure. In order to investigate the discrepancy this paper provides comparisons of signals recorded at the same time in free‐field and on foundations of structures during the same earthquake event. The signals used were obtained from four different built environments experimentally tested in centrifuges and on shaking tables in Japan, Europe and Canada.

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)0.000
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.258
Teacher spread0.243 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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