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Record W2074436467 · doi:10.3141/2004-07

Wireless Shape-Acceleration Array System for Local Identification of Soil and Soil Structure Systems

2007· article· en· W2074436467 on OpenAlexaff
Victoria Bennett, Mourad Zeghal, Tarek Abdoun, L. Danisch

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2007
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsGovernment of New Brunswick
FundersNational Science Foundation
KeywordsCentrifugeAccelerationBoundary value problemIdentification (biology)Geotechnical engineeringSoil structureBoundary (topology)Inverse problemSystem identificationComputer scienceBiological systemSoil waterEngineeringSoil scienceEnvironmental scienceMathematicsData miningPhysics

Abstract

fetched live from OpenAlex

Soil and soil structure systems are massive multiphase particulate systems characterized by the development of localized response mechanisms under extreme loading conditions. Identification and analysis of such systems on the basis of inverse boundary value problem formulations and sparse measurements are generally indeterminate. An alternative local inverse problem technique using measurements from the newly developed wireless shape-acceleration array (WSAA) system is presented. Local identification analyses of the constitutive behavior of water-saturated soil and soil structure systems are performed with acceleration and pore pressure records provided by a cluster of closely spaced sensors. The developed novel technique does not require the availability of boundary condition measurements or the solution of an associated boundary value problem. The constitutive behavior at a specific location of a soil or soil structure system is analyzed independently of adjacent response mechanisms or material properties. Numerical simulations and centrifuge tests of a soil-retaining wall system were used to demonstrate the capabilities of this local system identification technique. The combination of the developed WSAA and local identification technique constitute a major step toward autonomous monitoring technology and analysis tools capable of providing a realistic picture of large deformation and acceleration response or failure of soil and soil structure systems.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.368
Teacher spread0.307 · 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 designObservational
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
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicStructural Health Monitoring TechniquesFrench-language works237,207