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Record W3000048043 · doi:10.1139/cgj-2019-0583

Response amplification of back-rotated piles

2020· article· en· W3000048043 on OpenAlexvenueno aff
Wei Dong Guo

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersGriffith University
KeywordsPierGeotechnical engineeringStructural engineeringBending momentStiffnessRotation (mathematics)Displacement (psychology)Shear forceGeologySingularityAbutmentAmplification factorEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

Piles are largely back-rotated in sliding slope or subjected to lateral spreading. This paper reveals for the first time that response of these piles (e.g., displacement, rotation, bending moment, and shear force) is amplified against forward rotating piles. In particular, magnification is detrimental, once normalized rotational stiffness (NRS) of the piles is around a singularity value (i.e., normalized singularity stiffness, NSS). New expressions are developed to gain the NSS value, the magnification degree, and the sliding depth to incur the singularity. The NRS is assessed using 1g model tests. The solutions are adopted to capture the response of the model piles, to detect new failure mechanism of Showa Bridge, and to check the safety of Christchurch bridges. The main conclusions are as follows: (i) piles are prone to response amplification, when subjected to lateral spreading or in sliding slopes. (ii) The NRS is only slightly affected by soil movement profiles and sliding depths. (iii) Showa Bridge collapsed from displacement amplification of back-rotated piles. Finally, (iv) the roller connections between girder and piers, and an integral abutment and piers are proved to be effective to curb the amplification. The amplified response needs to be assessed in practice to lessen failure of back-rotated piles.

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: none
Teacher disagreement score0.609
Threshold uncertainty score0.575

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)0.0000.000

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 head0.013
GPT teacher head0.198
Teacher spread0.185 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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