MétaCan
Menu
Back to cohort
Record W4246946270 · doi:10.22215/etd/2021-14532

Performance Assessment of RC Columns Under Near-Field Blast Loading Using CFD Modelling

2021· dissertation· en· W4246946270 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

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsCarleton University
FundersStrong
KeywordsStructural engineeringExplosive materialDetonationComputational fluid dynamicsTransverse planeBlast waveEngineeringDisplacement (psychology)Cross section (physics)ResidualMaterials scienceComputer scienceShock wavePhysics

Abstract

fetched live from OpenAlex

This study investigates the effects of various design parameters on the performance of reinforced concrete columns under near-field blast loads, specifically for scaled distances less than 0.4 m/kg 1/3 . The computational fluid dynamics analysis method in LS-DYNA is used to model the detonation process of the explosive, the propagation of the blast wave, and its interaction with the structure. The model's ability to accurately predict blast loads and simulate structural response is verified against experimental data from the literature. Using the verified model, the influence of transverse reinforcement spacing, concrete cover, axial load ratio, and column cross-section shape on the structural performance is evaluated based on several parameters including the lateral displacement, the extent of the damage zone, material stress condition, and residual axial capacity.

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 categoriesMeta-epidemiology (narrow)
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 score0.040
Threshold uncertainty score1.000

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)0.0010.000

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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicStructural Response to Dynamic LoadsFrench-language works237,207