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Record W7115701579 · doi:10.71846/18-wcee-1403

IMPORTANCE OF SHEAR WAVE VELOCITY PROFIL ACCURACY IN DYNAMIC RESPONSE ANALYSIS

2025· article· en· W7115701579 on OpenAlexaboutno aff

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWave velocityLiquefactionResponse analysisEarthquake engineeringShear (geology)Field (mathematics)Surface waveCoherence (philosophical gambling strategy)Seismic analysis

Abstract

fetched live from OpenAlex

The demand for a precise evaluation of shear wave velocity Vs, is gaining interest in the field of geotechnical engineering due to its importance as a key parameter required to properly evaluate typical characteristics of soils. Field tests of Vs measurements have been used more often due to the accessibility of new techniques, such as the SCPT test, and the availability of geophysical methods using surface waves (e.g., MASW and MMASW). However, the analysis of the obtained data is a very difficult task that requires specialized equipment and technical expertise to assure that the measured data are properly handled and interpreted. Also, a proper data treating are of outmost importance where a great precision in the calculation of the deposit response is required such as in liquefaction evaluation or earthquake ground response analyses. In these situations, it is recommended to verify the coherence between the obtained geophysical (Vs) and geotechnical (N-SPT, qc-CPT) measurements using alternative methods (e.g., Vs-correlations, H/V method, etc.). In some situations, there may be consensus between the measurements that makes it easier for the designer to set dependably the seismic wave profiles. In other cases, geophysical and geotechnical test would provide very different resolutions for Vs measurements, an issue that complicates the decision of the practitioner. In this paper, a case study of Vs profiles measurements using MASW, MMASW, and SCPT tests at the Sorel-Tracy site, Quebec have been detailed, analysed, and discussed. Vs-qc and Vs-N correlations as well as the H/V spectral method have been utilized to verify the measured Vs profiles to assist the designer engineer in reaching an appropriate decision.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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