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Record W4403848444 · doi:10.1080/15583058.2024.2419545

Shake-Table Testing of a Brick Masonry Groin Vault: Overview of Blind Predictions and Postdictions and Comparison with Experimental Results

2024· article· en· W4403848444 on OpenAlexaff
Chiara Calderini, Nicoletta Bianchini, Paulo B. Lourénço, Nuno Mendes, Paulo Candeias, Omar AlShawa, César Chácara, Marcos M. Chávez, Gianmarco de Felice, Angela Ferrante, Paolo Fici, Raffaele Gagliardo, Prabakaran Kesavan, Gian Piero Lignola, Marialaura Malena, Daniele Malomo, Daniele Pellegrini, Fernando Mikelarena Peña, Massimo Petracca, Barbara Pintucchi, Francesco Portioli, Bora Pulatsu, Giancarlo Ramaglia, Davide Rapicavoli, Francesco Trovatelli

Bibliographic record

VenueInternational Journal of Architectural Heritage · 2024
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsCarleton UniversityMcGill University
FundersFundação para a Ciência e a TecnologiaAgence Nationale de la Recherche
KeywordsVault (architecture)MasonryShakeEarthquake shaking tableStructural engineeringEngineeringGeotechnical engineeringForensic engineeringGeologyMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents the results of the blind test competition carried out within the scope of the European project SERA.ta “Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations”. The purpose of the competition was to predict the results of a set of tests carried out on an unstrengthened full-scale cross vault with mortar joints and solid bricks (then strengthened with textile reinforced mortar) subjected to a horizontal dynamic excitation. The paper offers an overview of the modelling approaches utilised, along with their corresponding predictions and post dictions. The findings are assessed based on both the damage mechanisms and predicted values for displacements and accelerations in both directions. The results are then compared with the experimental findings. Modelling approaches utilizing the Finite Element Method (FEM) yielded the most accurate predictions regarding displacements and crack patterns. Conversely, a submission employing a Discrete Element model provided the most accurate prediction of damage mechanisms. Nonetheless, the significant discrepancies in predicted displacements and accelerations underscore the necessity for continued efforts to establish consensus on appropriate modelling assumptions for masonry vaults.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.367

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 communication0.0000.000
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 head0.027
GPT teacher head0.279
Teacher spread0.252 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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