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Record W2014020367 · doi:10.1139/l00-071

Damage detection in framed buildings

2001· article· en· W2014020367 on OpenAlexvenueno aff
J. Alberto Escobar, J Jesús Sosa, Roberto Gómez

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
KeywordsStructural engineeringTransformation (genetics)Frame (networking)StiffnessRepresentation (politics)Computer scienceMatrix (chemical analysis)EngineeringMaterials science

Abstract

fetched live from OpenAlex

The transformation matrix method for damage detection in structural elements of frame buildings, expressed as the loss of stiffness, is proposed and evaluated. The method, which allows the locating and assessing of the damage magnitude of structural elements by considering the contribution of each of them to the overall performance of the structure, is applicable to two- and three-dimensional building frames of several storeys and bays with one or several damaged elements. Effects of uncertainties in the experimental measurements of the dynamic characteristics and in the precision of the numerical representation of the structure on the method proposed are evaluated. A three-dimensional frame model with different simulated damage states and a reinforced concrete plane frame model damaged using an earthquake record as excitation are studied. Results show the good agreement between the estimated damage computed with the proposed method and the true value of damage.Key words: damage detection, transformation matrix, structural damage, damage assessment.

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 score0.649
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.217
Teacher spread0.208 · 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

Citations14
Published2001
Admission routes1
Has abstractyes

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