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Record W4387665063 · doi:10.1139/cjce-2023-0029

Estimating ice forces on a bridge pier using field observations and a deformation model

2023· article· en· W4387665063 on OpenAlexvenueno aff
Einar Rødtang, Knut Alfredsen, Knut V. Høyland

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicScientific Research and Discoveries
Canadian institutionsnot available
Fundersnot available
KeywordsPierRange (aeronautics)Structural engineeringEngineeringHindcastStandard deviationDeformation (meteorology)Bridge (graph theory)GeologyGeodesyGeotechnical engineeringMeteorologyMathematicsPhysicsStatisticsClimatology

Abstract

fetched live from OpenAlex

This technical note qualitatively and quantitatively describes the ice run that took place in Sokna river, Trøndelag, Norway on 23 January 2020. Metrological and hydrological data are described. Stranded ice floe size distribution data are presented and analysed. It is shown that the maximal ice floe dimension is well described by generalized exponential (shape parameter = 2.77, scale parameter = 1.42) or log-logistic distributions (alpha = 3.57, lambda = 1.15). A custom-built steel load panel had been mounted on Sokna bridge preceding the ice run; however, due to technical difficulties no forces were recorded. Hindcast calculations using Solidworks based on the permanent deflections experienced by this load panel have been carried out to estimate ice forces experienced by Sokna bridge. These hindcast calculations conclude that the real peak ice force experienced by the 0.8 m diameter cylindrical bridge piers must have been within the range 1.1 to 5.5 MN. This range is compared to predictions made by standards for predicting ice forces on bridge piers. This comparison reveals that the Norwegian standard, the lower bound of the SNiP standard, and the upper bound of the CSA S6:19 standard are consistent with this range, while the Swedish and Finnish standards predict too low forces.

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 score0.200
Threshold uncertainty score0.185

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 head0.044
GPT teacher head0.261
Teacher spread0.217 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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