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Record W4230593715 · doi:10.32920/ryerson.14656419

Crashworthiness of Traffic Light Steel Poles in Vehicle Collisions

2021· preprint· en· W4230593715 on OpenAlexafffundabout
Preveen Kumar Siriya

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsToronto Metropolitan University
FundersTransport Canada
KeywordsCrashworthinessAutomotive engineeringFinite element methodCollisionEngineeringProcess (computing)DoorsSide impactBridge (graph theory)Structural engineeringComputer science

Abstract

fetched live from OpenAlex

Vehicle crashworthiness focuses on the capability of a vehicle to protect its occupants in a collision. The Canadian Highway Bridge Design Code [2] does not provide design criteria for vehicle occupant safety except by field testing. The test-guided product development process is very costly and time-consuming. As an alternative, computer simulation tools are increasingly being used. The aim of this research is to contribute to the efficient design of traffic light poles by developing an experimentally calibrated, computer-based, finite-element model using LSDYNA [54], capable of predicting accurately their response when subjected to vehicle impact. The case of steel pole embedded directly in soil was proved to be strong enough to offer protection under service loading and vehicle impact. Side impact crashed proved to be more severe for the vehicle occupant as a result of the weak structural performance of the side doors of the vehicle. Based on this an innovative pole supported on a hard rubber base is introduced to improve crashworthiness.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.693

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations0
Published2021
Admission routes3
Has abstractyes

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