MétaCan
Menu
Back to cohort
Record W1773899353

Experimental Phase II of the Structural Health Monitoring Benchmark Problem

2003· book-chapter· en· W1773899353 on OpenAlexaff
Shirley J. Dyke, Dionisio Bernal, James L. Beck, Carlos E. Ventura

Bibliographic record

VenueCaltechAUTHORS (California Institute of Technology) · 2003
Typebook-chapter
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation
KeywordsShakerStructural health monitoringStructural engineeringHammerFrame (networking)BracingBenchmark (surveying)Experimental dataEngineeringSession (web analytics)AccelerometerVibrationComputer scienceAcousticsMechanical engineeringPhysicsMathematicsGeology
DOInot available

Abstract

fetched live from OpenAlex

This paper introduces the second experimental phase of the activities of the IASC-ASCE Structural Health Monitoring Task Group, involving the application of structural health monitoring techniques to data obtained from a four story steel frame structure tested in August 2002 at the University of British Columbia.These Phase II experimental studies follow a series of analytical studies focusing on a model of the same structure.In the experiment, damage was simulated by removing bracing or loosening bolts within the structure.Three types of excitation were considered: electrodynamic shaker, impact hammer, and ambient vibration.In the shaker tests an electrodynamic shaker on the top floor of the frame was used to excite the structure.Accelerometers were placed throughout the structure to provide measurements of the structural responses.The data and a complete description of the experimental setup are also available at http://wus-ceel.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations80
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueCaltechAUTHORS (California Institute of Technology)Same topicStructural Health Monitoring TechniquesFrench-language works237,207