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Record W3115733710 · doi:10.1139/cjce-2020-0059

Investigating the effects of inelastic soil–foundation interface response on the seismic demand of soil–structure systems

2020· article· en· W3115733710 on OpenAlexvenueno aff
Farshad Homaei

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSuperstructureFoundation (evidence)Soil structure interactionStructural engineeringGeotechnical engineeringShallow foundationNonlinear systemReduction (mathematics)Displacement (psychology)Interface (matter)EngineeringFinite element methodMechanicsPhysicsMathematicsGeometryBearing capacity

Abstract

fetched live from OpenAlex

The effect of inelastic response of the soil–foundation interface is explored on the seismic demand of structures attached on top of shallow foundations. An ensemble of 20 strong ground motions recorded on National Earthquake Hazards Reduction Program site class D was employed for analyzing soil–structure systems with the Winkler foundation model and an equivalent single-degree-of-freedom system as the superstructure. Results show that there are key parameters that control the amount of difference between the elastic and inelastic modeling of the soil at the soil–foundation interface. Depending on the structural aspect ratio, the elastic modeling leads to an overestimated result for the total lateral displacement demands. Also, more than 50% reduction in the superstructure demands is desired when nonlinear soil modeling is considered and foundation sliding is allowed. The benefits from the “shrinking-dominated rocking motion” can be acquired with inelastic soil material that limits the transferred inertial force into the superstructure.

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)0.001
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 score0.016
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

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 head0.006
GPT teacher head0.173
Teacher spread0.167 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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