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Record W4392506349 · doi:10.1061/9780784485330.010

Centrifuge Modeling of Soil-Structure Interaction with MICP Improved Soil

2024· article· en· W4392506349 on OpenAlexaboutno aff
Soo-Min Ham, Alexandra Camille San Pablo, Jose Luis Caisapanta, Jason T. DeJong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsCentrifugeSoil scienceEnvironmental scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

Liquefaction-induced settlement due to earthquakes can cause significant damage to supported infrastructure, with the settlement mechanisms mobilized depending on the characteristics of the soil profile, ground motion, and structure. Microbially induced calcite precipitation (MICP) has been shown to mitigate liquefaction and reduce settlements in previous centrifuge studies. This study conducted a series of 1-m centrifuge tests at the University of California, Davis (UCD) Center for Geotechnical Modeling (CGM) to evaluate soil-structure interaction (SSI) of a simple foundation system on loose Ottawa F65 sand (Dr < 40%) with a finite MICP-treated zone. A simple rigid structure, designed to be susceptible to rocking when subjected to a 1-Hz motion, was used. A high-speed camera motion tracking system was developed to measure the lateral and vertical movements of the structure during shaking. This non-contact system eliminated all instrumentation physically connected to the structure, which ensured that the measurement system did not affect the structure performance. Accelerometers and pore pressure transducers were embedded within the underlying soil to capture the dynamic system response. Results show how a finite MICP-treated zone beneath a structure can help reduce the structure settlement and loading experienced.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.995

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)0.0050.000

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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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