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Record W2475774390 · doi:10.1155/2016/4167329

Modelling Blast Effects on a Reinforced Concrete Bridge

2016· article· en· W2475774390 on OpenAlexaff
Markellos Andreou, Anastasios Kotsoglou, S. J. Pantazopoulou

Bibliographic record

VenueAdvances in Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsYork University
Fundersnot available
KeywordsStructural engineeringBridge (graph theory)Displacement (psychology)Computer scienceFinite element methodBlast waveDeformation (meteorology)Beam (structure)EngineeringGeology

Abstract

fetched live from OpenAlex

The detailed investigation of blast phenomena and their catastrophic effects on existing structures are the main objectives of the present paper. It is well known that blast phenomena may be characterized by significant complexity, often involving complicated wave propagation effects as well as distinguishable material behaviors. Considering the above and in an attempt to provide a simplified modelling approach for the simulation of blast effects, a novel procedure is presented herein based on well-established methodologies and common engineering practices. In the above framework, firstly, the “predominant” deformation shape of the structure is estimated based on elastic finite element simulations under blast loads and then the structural response of the system is evaluated as a result of common computational beam-element tools such as displacement-based pushover analysis. The proposed methodology provides an immediate first estimation of the structural behavior under blast loads, based on familiar engineering procedures. A two-span reinforced concrete bridge was thoroughly investigated and the results provide insightful information regarding the damage patterns and localization.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2016
Admission routes1
Has abstractyes

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