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Record W2767808870 · doi:10.2495/safe-v7-n4-475-485

Seismic rehabilitation of masonry heritage structures with base-isolation and with selected contemporary strengthening measures

2017· article· en· W2767808870 on OpenAlexvenueno aff
Vojko Kilar, Simon Petrovčić

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
FundersJavna Agencija za Raziskovalno Dejavnost RS
KeywordsMasonryUnreinforced masonry buildingIsolation (microbiology)RehabilitationForensic engineeringBase isolationBase (topology)EngineeringCivil engineeringStructural engineeringMedicinePhysical therapyMechanical engineeringBioinformatics

Abstract

fetched live from OpenAlex

In the seismically active zones of southern and central Europe, unreinforced masonry (URM) structures are one of the most common types of buildings. Some of them possess a high historical value and could therefore be classified as part of the architectural heritage, requiring special attention with regard to their preservation and retrofitting measures. In practice, however, the accurate prediction of the seismic response of such structures often proves to be difficult, not only due to the complex geometrical features of their individual architectural parts, but also due to the composite and non-homogeneous nature of URM. Due to the great variety of structural configurations and materials used, rational approaches for the assessment of the seismic safety levels of such buildings are needed. The paper analyses the applicability of two contemporary seismic strengthening measures, namely the use of fibre-reinforced polymer composites and the implementation of base-isolation to achieve the desired, code-based seismic protection levels. A three-storey masonry building was considered in the study. Non-linear static analyses for different levels of seismic intensities were conducted on a mathematical model of the fixed-base, FRP-strengthened and base-isolated variants of the 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 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 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 score0.114
Threshold uncertainty score0.402

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.194
Teacher spread0.188 · 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 teacher head, 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

Citations4
Published2017
Admission routes1
Has abstractyes

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