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Record W3020522092 · doi:10.1177/2041419620912751

Design guidelines and optimization of ultra-high-performance fibre-reinforced concrete blast protection wall panels

2020· article· en· W3020522092 on OpenAlexafffund
Mohtady Moataz Sherif, H. Othman, H. Marzouk, Hassan Aoude

Bibliographic record

VenueInternational Journal of Protective Structures · 2020
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsUniversity of OttawaToronto Metropolitan University
FundersNational Research Council Canada
KeywordsStructural engineeringFinite element methodDurabilityToughnessStiffnessMaterials scienceDuctility (Earth science)Flexural strengthReinforcementUltimate tensile strengthComposite materialEngineeringCreep

Abstract

fetched live from OpenAlex

Ultra-high-performance fibre-reinforced concrete is the latest generation of structural concrete, having outstanding fresh and hardened properties; this includes the ease of placement and consolidation with ultra-high mechanical properties, as well as toughness, volume stability, durability, higher flexural and tensile strength, and ductility. As more research is being focused on it, the material behaviour and characteristics are getting more understood, and the research demand for the special applications of the ultra-high-performance fibre-reinforced concrete is growing higher. One special application that ultra-high-performance fibre-reinforced concrete is thought to have an outstanding performance at is in the field of protective structures, specifically against blast loads. This article presents part of a study that is concerned with the behaviour and response of ultra-high-performance fibre-reinforced concrete wall panels under blast load. Size and shape optimization techniques were combined in this study to optimize the design of a 200-MPa ultra-high-performance fibre-reinforced concrete under blast loads using finite element modelling. This design optimization aims to maximize stiffness and minimize the cost while satisfying both design stresses and construction requirements. The design variable to be optimized for are the thickness ranging from 100 to 300 mm at 25 mm increments, in addition to the reinforcement ratio of 0%, 0.2%, 1% and 3%, and aspect ratio of 1, 1.5 and 2; the boundary condition is four edges fixed and restrained. The numerical simulation has been performed using an explicate finite element software package. The complete behaviour of an ultra-high-performance fibre-reinforced concrete is defined using the concrete damaged plasticity model. The concrete constitutive model has been developed considering the contribution of tensile hardening response, fracture energy and crack-band width approaches to accurately represent the tensile behaviour and guarantee mesh independence of results. The blast load is applied using the Conventional Weapons method of the US Army Corps of Engineers that is readily available in the finite element software. The validity of the numerical model used is verified by comparing numerical results to experimental data.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.252
Teacher spread0.227 · 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
GenreMethods

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

Citations8
Published2020
Admission routes2
Has abstractyes

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