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Record W2725540891 · doi:10.1061/9780784480786.015

Compaction Grouting as Part of Seismic Retrofit of Two Bridges in British Columbia, Canada

2017· article· en· W2725540891 on OpenAlexaffabout
Thuraisamy Thavaraj, Alex Sy

Bibliographic record

VenueGrouting 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsKlohn Crippen Berger (Canada)
Fundersnot available
KeywordsGeotechnical engineeringCompactionGeologyGroutLeveeLiquefactionPileAbutmentEngineeringCivil engineering

Abstract

fetched live from OpenAlex

This paper describes the successful applications of compaction grouting as part of the seismic retrofits of two major bridges across the Fraser River in British Columbia, Canada. At both sites, liquefaction of deep post-glacial river sediments consisting of loose fine sand with trace to some silt is the key issue that affects the seismic performance of the bridge. At Mission Bridge, the south approach piers are founded on timber pile groups. Analyses showed that liquefaction of a 2 m to 3 m thick sand layer beneath the pile toes at 17 m depth would cause unacceptable settlements of the piers. Compaction grouting was consequently used to create an annular ring-shaped densified zone beneath the pile foundations. Grout casings were advanced at 9° inclination to reach the loose zone beneath the pile toes, and grouting was conducted with target volume and limiting pressure established based on trials. At Knight Street Bridge, the south abutment is supported on spread footing founded on liquefiable sand. A horseshoe-shaped ground densification zone around the abutment embankment was designed to reduce liquefaction-induced displacements. Timber compaction piles were used for densification outside the bridge deck. Under the deck, because of the limited headroom (~5 m), compaction grouting to 16 m depth was implemented. Cone penetration tests were conducted at both sites to confirm the effectiveness of compaction grouting densifications.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.571

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 integrity0.0000.000
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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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