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Record W2179419111 · doi:10.1193/122912eqs361m

Local Strengthening of Reinforced Concrete Structures as a Strategy for Seismic Risk Mitigation at Regional Scale

2014· article· en· W2179419111 on OpenAlexaff
Raffaele Frascadore, Marco Di Ludovico, Andrea Prota, Gerardo Mario Verderame, Gaetano Manfredi, Mauro Dolce, Edoardo Cosenza

Bibliographic record

VenueEarthquake Spectra · 2014
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsCouncil of Ministers of Education
Fundersnot available
KeywordsReinforced concreteVulnerability (computing)Work (physics)Seismic retrofitUpgradeCivil engineeringFibre-reinforced plasticScale (ratio)Seismic riskEarthquake scenarioUrban seismic riskEngineeringForensic engineeringStructural engineeringSeismic hazardComputer scienceGeography

Abstract

fetched live from OpenAlex

Recent earthquakes have clearly shown the high vulnerability of existing reinforced concrete (RC) structures. There is a crucial need to find cost‐effective and efficient strategies and methods to strengthen a large number of buildings (i.e., at a regional scale) before future major earthquakes occur. A viable strategy to prevent potential damage caused by earthquakes could be to selectively upgrade local capacity of individual structural components. In the aftermath of the 6 April 2009 L'Aquila earthquake, local retrofit work based on the use of fiber polymer reinforcement (FRP) were executed to increase the seismic capacity of lightly damaged public and private buildings. According to theoretical analyses carried out on six reinforced concrete (RC) school buildings in L'Aquila, a seismic safety level of about 60% of that requested in the design of a new building can be achieved in most cases by FRP‐based strengthening of exterior joints.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations54
Published2014
Admission routes1
Has abstractyes

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