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Record W4376117920 · doi:10.1080/15397734.2023.2207621

Structural design and optimization of a guardrail for the train-to-train collision test platform

2023· article· en· W4376117920 on OpenAlex

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

VenueMechanics Based Design of Structures and Machines · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Hunan Province
KeywordsTopology optimizationCollisionDimensionless quantityStructural engineeringNormalization (sociology)Computer scienceReduction (mathematics)Topology (electrical circuits)SimulationEngineeringMathematicsFinite element methodGeometryPhysicsMechanics

Abstract

fetched live from OpenAlex

The guardrail structure is generally installed in parallel on the inner side of the basic rail, which is expected to prevent the wheelsets of rail vehicles from derailing during the train-to-train collision test. This study aims to conduct research on the structural design and optimization of the guardrail for the train-to-train collision test platform. A two-step optimization method was employed, i.e., the topological optimization design of the supporting post and the lightweight size optimization of the overall guardrail, to improve the material utilization rate and achieve the best space size design. In order to explore the topology optimization of the supporting post alone and the overall topology optimization of the guardrail (i.e., a combination of supporting post and central plate), two optimization models were designed with seven subcases each loaded with different heights (H_Fc). Through comparative analysis, the selected supporting post under the H_Fc of 650 mm exhibited a mass reduction rate of 69.597%. In order to explore the optimal configuration of the guardrail and the spacing arrangement of supporting posts, a lightweight size optimization design based on topological result was carried out. The supporting post condition and the central plate condition were considered according to the different positions where the guardrail may be impacted by the wheelset, and the normalization method was adopted to make the output responses dimensionless. The optimization results were obtained by implementing the Box Behnken method, the Hammersley method, response surface model and genetic algorithm. The optimized structure showed that its mass per unit length is 43.885% lighter than the initial configuration. Compared with the conceptual design, the optimized structure was 3.564% lighter, while the maximum deformation was reduced by 9.049%. Therefore, the lightweight optimized guardrail has higher protection strength and lower mass, which is expected to be widely used for the train-to-train collision test platform.

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: Methods · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.694

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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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