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Record W3082309124 · doi:10.24425/ace.2020.134394

Mitigation of Blast Load Risk on Reinforced Concrete Structures Considering Different Design Alternatives

2020· article· en· W3082309124 on OpenAlexaff
Yasser E. Ibrahim, Monjee K. Almustafa

Bibliographic record

VenueArchives of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsWestern University
Fundersnot available
KeywordsStructural engineeringReinforced concreteEngineeringCivil engineeringComputer scienceForensic engineering

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate a structure’s response to blast loading when composite columns are used instead of conventional reinforced concrete (RC) cross sections and when a conventional structure is retrofitted with braces. The study includes conducting dynamic analyses on three different structures: a conventional reference RC structure, a modified structure utilizing composite columns, and a modified structure retrofitted with steel braces. The two modified structures were designed in order to investigate their performance when subjected to blast loading compared to the conventional design. During the dynamic analyses, the structures were exposed to simulated blast loads of multiple intensities using the finite-element modelling software, SeismoStruct. To evaluate their performance, the responses of the modified structures were analyzed and compared with the response of the conventional structure. It was concluded that both the structure with composite columns and the steel brace structure experienced less damage than the conventional model. The best performance was obtained through the steel brace structure.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.195
Teacher spread0.186 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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