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Record W4221033462 · doi:10.1061/9780784484043.023

A Simplified Approach to Model Deck Resistance and Back Rotation of Abutments due to Lateral Spreading

2022· article· en· W4221033462 on OpenAlex
Lalinda Weerasekara

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeo-Congress 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsSuperstructureDeckStructural engineeringAbutmentPileRotation (mathematics)Geotechnical engineeringBridge (graph theory)EngineeringComputer science

Abstract

fetched live from OpenAlex

Past earthquakes have indicated that bridge superstructure tends to restrain the movements at the top of the abutments (or piers) while lateral spreading tends to push the piles towards the direction of soil movement. This leads to the back-rotation of abutment and piles, which is different to the pile response estimated using traditional fixed or free pile head conditions. Although, the superstructure (deck) resistance is considered in simplified methods such as the Caltrans (2012) approach, the analysis cannot mimic the pile/abutment back-rotation besides several other limitations related to the approach used to account for the deck resistance. This paper introduces a modified approach to overcome these limitations. The revised approach is based on the same basic framework as in Caltrans (2012) but modifications are introduced to properly account for the deck resistance. A user-defined “deck spring” is introduced into the pushover analysis to represent the superstructure resistance instead of applying it to the slope stability model. The differences between these two approaches are highlighted using an example.

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.

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 score0.048
Threshold uncertainty score0.705

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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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