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Record W2328638167 · doi:10.1139/cgj-2011-0083

Seismic soil–structure interaction in buildings on stiff clay with embedded basement stories

2013· article· en· W2328638167 on OpenAlexaffvenue
Alper Turan, Sean D. Hinchberger, M. Hesham El Naggar

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern UniversityHatch (Canada)Ministry of Transportation of Ontario
Fundersnot available
KeywordsEmbedmentSoil structure interactionBasementGeotechnical engineeringStructural engineeringEarthquake shaking tableFoundation (evidence)Finite element methodGeologyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

The increasingly popular performance-based design approach requires that soil–structure interaction (SSI) analysis become an integral part of the seismic evaluation. This is particularly important for structures with substantial embedment. The primary objectives of this study are twofold: (i) evaluate the SSI effects for buildings with a basement and (ii) evaluate the ability of two analytical methods to account for SSI effects in seismic design — an analytical solution for kinematic SSI and a nonlinear finite element model. Scaled model shaking table tests were performed on a model building with an embedded basement founded in a synthetic stiff clay deposit enclosed in a laminar soil container. The model structure used in this study comprised a simple single-degree-of-freedom structure with a modular box foundation designed to permit consideration of structures with different basement embedment depths. The experimental results showed that the ratio of effective period of the soil–structure system to that of the structure ([Formula: see text]) decreased for the long-period structure and increased for the short-period structure, with increasing embedment. The results confirm the ability of the analytical techniques to predict with reasonable accuracy the SSI effects for buildings with embedded parts.

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 score0.045
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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

Citations35
Published2013
Admission routes2
Has abstractyes

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