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Record W4404642981 · doi:10.1016/j.soildyn.2024.109101

Performance-based liquefaction analysis and probabilistic liquefaction hazard mapping using CPT data within the Fraser River delta, Canada

2024· article· en· W4404642981 on OpenAlexaffabout
Alireza Javanbakht, Sheri Molnar, Abouzar Sadrekarimi

Bibliographic record

VenueSoil Dynamics and Earthquake Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsWestern University
Fundersnot available
KeywordsLiquefactionHazardGeologyDeltaRiver deltaGeotechnical engineeringSeismologyEngineering

Abstract

fetched live from OpenAlex

Since the Fraser River delta is affected by multiple seismic sources, the selection of a single combination of magnitude and maximum ground acceleration to evaluate liquefaction initiation is challenging. In this study, we use a probabilistic seismic hazard analysis to account for all earthquake scenarios and also consider the liquefaction model uncertainty and soil resistance uncertainty using 787 CPT data across the study area. Hazard curves are generated for the factor of safety against liquefaction (FS L ) and the amount of required soil improvement to prevent liquefaction (Δq L ) which provide a comprehensive assessment of liquefaction triggering. We derive the FS L and Δq L values corresponding to return periods of 475 and 2475 years and generate the first probabilistic liquefaction hazard mapping for the region. Most of the lowland areas of Metro Vancouver correspond to low FS L and therefore soil improvement is needed for liquefaction mitigation. • A performance-based liquefaction analysis accounts for all earthquake hazards. • Key parameter uncertainities are accounted for in the presented probabilistic liquefaction methodology. • The probabilistic liquefaction hazard mapping offers a preliminary assessment for soil improvement 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.186
Teacher spread0.176 · 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 designSimulation or modeling
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

Citations5
Published2024
Admission routes2
Has abstractyes

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