MétaCan
Menu
Back to cohort
Record W1966908301 · doi:10.1115/omae2010-20069

On the Use of Mode Shapes in the Identification of Structural Damage

2010· article· en· W1966908301 on OpenAlexaff
Ahmed A. Elshafey, M.R. Haddara, H. Marzouk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsToronto Metropolitan UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsMode (computer interface)ModalFinite element methodNormal modeIdentification (biology)Modal analysisComputer scienceExcitationAlgorithmBiological systemStructural engineeringAcousticsVibrationMaterials sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

The random decrement technique is an averaging technique that can be used to extract the free decay response of a structure from its stationary random vibratory response. The free decay response can then be used to identify the vibratory characteristics of the structure. The main advantage of the technique is that the identification of the parameters of the structure is achieved without previous knowledge of the excitation forces. This paper extends the random decrement technique to obtain the mode shapes of the structure using the concept of a multichannel random decrement technique (MCRD). This technique is based on extracting simultaneous random decrement records from measurements made at several points on the structure. The change in mode shapes as a result of occurrence of damage to the structure is then used to detect the presence and location of damage. The method is illustrated and validated using two case studies and finite element modeling. Excellent agreement between mode shapes obtained using the MCRD and those obtained from classical modal analysis techniques was found. The use of changes in the mode shapes of the structure to detect the presence and location of damage was successful.

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 score0.548
Threshold uncertainty score0.112

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 head0.042
GPT teacher head0.312
Teacher spread0.269 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicStructural Health Monitoring TechniquesFrench-language works237,207