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Analytical Approach for Seismic Performance of Extended Pile-Shafts

2018· article· en· W2883830131 on OpenAlexaff
Mehdi Heidari, M. Hesham El Naggar

Bibliographic record

VenueJournal of Bridge Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsStructural engineeringPileHingeCurvatureCantileverPlastic hingeNonlinear systemGeotechnical engineeringDuctility (Earth science)Wedge (geometry)Displacement (psychology)Bridge (graph theory)EngineeringGeologyMaterials scienceMathematicsGeometryComposite material

Abstract

fetched live from OpenAlex

Seismic performance of bridge structures supported by extended pile-shafts principally depends on the curvature demand in critical regions of the pile below ground level. The equivalent fixed-based cantilever model is commonly used to assess the local curvature ductility demand of a yielding pile-shaft at any inelastic displacement level. In this approach, adequate prior knowledge of several parameters, including depth-of-fixity, plastic-hinge depth, and equivalent plastic-hinge length, is essential for proper estimation of ductility capacity. The present study aims to propose analytical formulations by using concepts of the strain wedge method based on nonlinear behavior of the soil-pile system to assess the key parameters of the equivalent cantilever model. The ability of the developed model in assessing the curvature ductility demand of the bridge system is validated against several published full-scale tests on RC shafts in clay and sand.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.228
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 source (direct Gemma or distilled Codex), 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

Citations28
Published2018
Admission routes1
Has abstractyes

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