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Record W4395685422 · doi:10.18280/ijsse.140224

Structural Design and Simulation of the Collision Between a Pedestrian's Head and a Windshield of Vehicle

2024· article· en· W4395685422 on OpenAlexvenueno aff
Mouad Garziad, Abdelmjid Saka, Hassane Moustabchir

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWindshieldPedestrianHead (geology)CollisionHead-up displayComputer scienceEngineeringTransport engineeringComputer securityGeologyAerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The vehicle's windshield plays an important role in the safety and comfort of drivers and passengers, so the windshield is solicited to many loads, such as the deformation of the vehicle body, the wind force, the crash between vehicles and vehicles, and a pedestrian.The primary goal of this study is to examine the mechanical and structural design of laminated glass for windshields in the scenario of an adult dummy head impact.The head impactor is represented by a spherical form covered in a rubber hull.The pedestrian head's Finite Element Method (FEM) is created per the requirements of Global Technical Regulations (GTRs).It weighs approximately 4 kg and strikes a windshield at a speed of 11.11 mm/ms.Furthermore, the windshield model is designed with two layers of glass with a layer of Polyvinyl Butyral (PVB).Our study's windshield Finite Element model is modeled with shell elements.The mathematical models of the PVB are described in a systematic manner.The model accurately represents the windshield's behavior, with critical fracture stress occurring in the impacted zone and maximum linear acceleration of the dummy head.The obtained results showed good agreement in energy absorption and maximum stress due to the impact.

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 score0.102
Threshold uncertainty score0.224

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 head0.012
GPT teacher head0.251
Teacher spread0.238 · 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
Published2024
Admission routes1
Has abstractyes

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