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Record W2536934250 · doi:10.11159/icsenm16.104

Determination of Modal Parameters of Historical Masonry Minarets by using Operational Modal Analysis

2016· article· en· W2536934250 on OpenAlexvenueno aff
Kemal Hacıefendioğlu, Gökhan Demir, Emre Alpaslan

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsModalMasonryOperational Modal AnalysisModal analysisComputer scienceModal testingStructural engineeringEngineeringMaterials scienceFinite element methodComposite material

Abstract

fetched live from OpenAlex

This study conducted to investigation of modal parameters (such as mode shapes, modal frequencies ) of a masonry type historical minaret by performing numerical and experimental analys es. Operational Modal Analysis (OMA) Technique is used for experimental study to obtain modal parameters of historical masonry minarets. For this purpose, Byk Mosque located in Samsun, Turkey, was chosen for the experimental analysis. The Frequency Domain Decomposition (FDD) method is utilized to identify the natural frequencies and mode shapes experimentally. ANSYS software is used to carry out 3D finite element modelling of the historical masonry minaret and determine the natural frequencies and mode shapes of the minaret analytically. Furthermore, the finite element model of the minaret is calibrated according to the experimental results by using t he Response Surface based finite element (FE) model calibration technique to obtain more accurate results of the modal parameters of the structure. The results of experimental, initial and calibrated finite element model were compared to each other. It can be noticed significant differences when comparing the results of the experimental and analytical with the initial conditions. Model calibration techniques are necessary to obtain more reasonable finite element model.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.615

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 head0.006
GPT teacher head0.179
Teacher spread0.174 · 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 designBench or experimental
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

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicMasonry and Concrete Structural AnalysisFrench-language works237,207