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Record W4232015957 · doi:10.1139/t01-067

Liquefaction performance of soils at the site of a partially completed ground improvement project during the 1999 Chi-Chi earthquake in Taiwan

2001· article· en· W4232015957 on OpenAlexvenueno aff
Der‐Her Lee, C. Hsein Juang, Chi-Sheng Ku

Bibliographic record

VenueCanadian Geotechnical Journal · 2001
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersNational Science CouncilNational Science Foundation
KeywordsLiquefactionPenetration testGeotechnical engineeringSeismic microzonationGeologyCone penetration testSoil liquefactionEnvironmental scienceSeismologySubgrade

Abstract

fetched live from OpenAlex

This paper examines the liquefaction performance of soils at the site of a partially completed ground improvement project at the Chang-Hwa Coastal Industrial Park during the 1999 Chi-Chi earthquake in Taiwan. The site is on land reclaimed by hydraulic filling. To meet the need of a planned construction, site characterization was carried out with standard penetration tests (SPTs) and cone penetration tests (CPTs) at 13 locations. Dynamic compaction was later performed to mitigate the potential liquefaction hazards at this site. Before completion of the ground improvement work, the site experienced a major earthquake, the Chi-Chi earthquake (magnitude M w = 7.6). Evidence of liquefaction was observed in this earthquake in the unimproved area but not in the improved area. After the earthquake, additional site exploration was carried out using SPTs and CPTs. The data from these in situ tests carried out before and after the earthquake and in areas with and without ground improvement are analyzed and the results are reported.Key words: ground improvement, in situ tests, liquefaction, earthquake.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.009
GPT teacher head0.193
Teacher spread0.184 · 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

Citations17
Published2001
Admission routes1
Has abstractyes

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