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Record W4225153526 · doi:10.1142/s0219455422710031

A Novel Approach to the Integration for Generating Consistent Ground Acceleration, Velocity and Displacement Time Histories

2022· article· en· W4225153526 on OpenAlexaff
Lanlan Yang, B. L. Ly, Wei‐Chau Xie, Chenxi Mao, Xiangnan Qin

Bibliographic record

VenueInternational Journal of Structural Stability and Dynamics · 2022
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesInstitute of Engineering Mechanics, China Earthquake AdministrationNatural Science Foundation of Jiangsu ProvinceChina Postdoctoral Science Foundation
KeywordsAccelerationDisplacement (psychology)EigenfunctionEigenvalues and eigenvectorsPhysicsMathematical analysisMathematicsClassical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

The velocity and the displacement time history obtained by directly integrating the acceleration time history will drift unrealistically because of the over-determinacy of the integration constants. Applying baseline corrections to resolve drift disturbs the frequency content and renders the corrected processes mutually inconsistent. In this study, eigenfunctions of sixth-order eigenvalue problem are introduced as basis functions for decomposing recorded ground motion time history. Taking advantage of the eigenfunctions and their first two order of differentiations are drift-free and consistent, the decomposed and reconstructed acceleration, velocity, and displacement time histories can be drift-free and mutually consistent without direct integration and baseline correction. Two earthquake ground motions are presented as examples, which show that the decomposed acceleration, velocity, and displacement time histories are drift-free, mutually consistent, and almost the same as the seed motion. The new approach can replace integral in the generation of velocity and displacement time histories.

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 score0.165
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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