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Record W1982610914 · doi:10.1139/t06-012

Long-term monitoring of SIDD Type IV installations

2006· article· en· W1982610914 on OpenAlexvenueaboutno aff
Lui Sammy Wong, Erez N. Allouche, Ashutosh Sutra Dhar, Michael Baumert, Ian D. Moore

Bibliographic record

VenueCanadian Geotechnical Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersMinnesota Department of Transportation
KeywordsGeotechnical engineeringEnvelope (radar)Term (time)EngineeringGeologyEnvironmental science

Abstract

fetched live from OpenAlex

An evaluation of the standard installation direct design (SIDD) prediction method has been undertaken by constructing and monitoring full-scale test beds installed according to SIDD Type IV specifications at four test sites across southern Ontario, Canada. Stresses around the test beds were monitored over a period of 20 months. The internal diameter of the test pipe segments varied from 600 mm to 900 mm; in situ soil conditions ranged from organic clay to sand, and burial depths varied from 1.5 to 3 times the diameter of the installed pipe. All test sections were subjected to frequent heavy traffic loads, representing a worse case loading scenario. Measurements from the 20 month monitoring period were compared with predictions from Ontario Provincial Standards and SIDD specifications. It was concluded that the SIDD method reasonably predicts the stress envelope around a buried rigid pipe installed using the cut-and-cover construction method. The indirect design method currently used by the Ontario Provincial Standards was found to provide an overly conservative prediction of soil stresses at the invert of the pipe. Field measurements also suggest that the value of the horizontal arching factor (HAF) currently recommended by SIDD for Type IV installations is overly conservative and can be increased while maintaining a conservative design approach.Key words: soil, pipe, interaction, rigid, SIDD, monitoring.

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 score0.927
Threshold uncertainty score0.617

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 integrity0.0000.001
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 head0.009
GPT teacher head0.205
Teacher spread0.197 · 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

Citations19
Published2006
Admission routes2
Has abstractyes

Explore more

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