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Record W2142753939 · doi:10.1139/t07-062

Seismic record analysis of the Liyutan earth dam

2007· article· en· W2142753939 on OpenAlexvenueno aff
Jin‐Hung Hwang, Chia-Pin Wu, S.-Y. Wang

Bibliographic record

VenueCanadian Geotechnical Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyShandong Academy of Medical Sciences
KeywordsGeologySeismologySettlement (finance)VibrationGround motionGeotechnical engineeringEarthquake simulationSeismic analysisSeismic waveSeismic microzonationAcoustics

Abstract

fetched live from OpenAlex

In this paper the authors present an analysis of the seismic records of the Liyutan dam, which is a zoned compacted earth dam. It is 96 m in height, and it suffered some minor cracks and settlement during the 1999 Chi-Chi earthquake. A large amount of seismic motion has been recorded for 42 past earthquake events. These records were used to analyze the vibration characteristics of the dam. The fundamental frequency and nonlinear amplification of the peak ground motion of the dam body have been clearly identified. Based on the fundamental frequency identified from the small earthquake data, the seismic motion of the dam during the Chi-Chi earthquake could be well simulated by an equivalent linear dynamic procedure. An empirical decomposition method was used to decompose the Chi-Chi earthquake motion histories, from the top and the bottom of the dam, into several orthogonal and complete intrinsic mode functions (IMFs). These IMFs show that the low frequency parts of the seismic motion at the top and bottom of the dam are nearly the same, however, the high frequency parts are significantly amplified when the seismic motion propagates from the bottom to the top of the dam.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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 designObservational
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

Citations16
Published2007
Admission routes1
Has abstractyes

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