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Record W7115711532 · doi:10.71846/18-wcee-0870

EFFECT OF GROUND MOTION PARAMETERS ON THE SEISMIC VERTICAL-TO-HORIZONTAL ACCELERATION RATIOS

2025· article· en· W7115711532 on OpenAlexaboutno aff

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMagnitude (astronomy)Peak ground accelerationEpicenterEarthquake magnitudeShear (geology)Ground motionAccelerationSeismic microzonation

Abstract

fetched live from OpenAlex

Recent studies clearly show the necessity of including the vertical component of earthquake ground motions in the seismic analysis and design of structures. This study investigates the effects of earthquake magnitude (Mw), epicentral distance (Repi), and soil condition according to the average shear wave velocity (Vs30) on both the vertical-to-horizontal (V/H) spectral accelerations (SA) and peak ground accelerations (PGA) in the Eastern Canada region. To this end, 248 sets of records were selected from 67 earthquakes that occurred in the studied seismic region on different soil conditions, with a magnitude (Mw) of 3.0 and above, and an epicentral distance (Repi) of less than 150 km. In all cases, the computed average trendlines of PGA and SA ratios were found to be higher than the empirical ratio of 2/3 recommended in the current NBC edition. Moreover, a significant increase in the PGA ratios was observed with larger earthquake magnitude (Mw), reduced distance from the epicentre (Repi), and smaller shear wave velocity (Vs30). Likewise, notable impacts of earthquake magnitude and soil condition on the V/H SA ratios were observed. In this regard, the average V/H SA ratio increased for periods up to 2.0 sec for larger earthquake magnitudes, and the ratio increased at soil conditions with smaller shear wave velocity (Vs30) for periods less than 0.5 sec. Finally, no correlation between the epicentral distance and the SA ratios was concluded.

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 score0.176
Threshold uncertainty score0.505

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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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