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Record W3011134939

DEVELOPMENT OF A VIBRATION-BASED HEALTH MONITORING STRATEGY FOR ONSHORE AND OFFSHORE PIPELINES

2013· article· en· W3011134939 on OpenAlexvenueno aff
Pejman Razi

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2013
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportSubmarine pipelineMarine engineeringEnvironmental scienceEngineeringForensic engineeringGeologyOceanographyMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Ageing mechanical, civil, aerospace, marine and offshore structures require continuous and accurate assessment on their integrity to avoid potentially hazardous failures. To further facilitate this crucial demand, a new technical terminology, generally referred to as structural health monitoring (SHM) has been coined in three past decades. SHM involves deployment of a sensory network on such structures in order to gather useful data, such that processing and interpreting the data through specific algorithms would enable one to detect defects and anomalies within the structures.\nThis dissertation presents the results of a series of efforts expended towards the refinement and enhancement of a vibration-based SHM technique, which was originated within our research group. In the adopted damage detection scheme, vibration data are gathered from structures via piezoelectric sensors. Data are processed by a robust signal processing approach, known as the empirical mode decomposition (EMD) in order to establish energy-based damage indices (EMD_EDIs). Interpretation of the damage indices enables detection of onset, location and advancement of defects within structures. \nA series of adjustments and modifications were devised and implemented to the application of the originally developed methodology, such that, besides increasing the methodology’s robustness and accuracy, they also facilitate a remote vibration-based SHM targeting onshore and offshore pipelines. \nThe integrity of the method in detection of bolt-loosening in a bolted flange joint of a full-scale pipeline was verified through numerical simulations and experimental investigations. The source of a significant inconsistency reported in the previous trials was identified and resolved. Also, for the first time, the remote application of the technique was facilitated by incorporating an advanced wireless data acquisition system. Moreover, the application of the methodology was extended to detection of cracks in girth-welds of offshore pipelines. In this regard, a comprehensive discussion is first provided, which identifies the role of parameters that influence the accuracy of numerical modeling of the dynamic response of submerged structures. The experimental and numerical investigation following the aforementioned modeling efforts presents encouraging results in detection of an advancing notch in the girth-weld of a submerged pipe. The use of a piezoelectric-based excitation technique, incorporated for the first time in the application of the methodology would evidence the enhanced practicality and robustness of the approach. The study concludes with a successful detection of a real-life sharp propagating crack in a beam due to cyclic loadings.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.998
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.199
Teacher spread0.187 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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Same venueLibrary and Archives Canada (Government of Canada)Same topicStructural Health Monitoring TechniquesFrench-language works237,207