MétaCan
Menu
Back to cohort
Record W2731592002 · doi:10.1061/9780784480809.022

Cement-Bentonite Slurry Walls for Seismic Containment of the Kingston Coal Ash Landfill

2017· article· en· W2731592002 on OpenAlexaff
Alan F. Rauch, Steve Artman, John C. Kammeyer, Bruce J. Haas, Jeffrey Barrett, Tom Pace, P. Bradford Smiley, Michael J. Steele, Yong Wu

Bibliographic record

VenueGrouting 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsStantec (Canada)Kensington Health
Fundersnot available
KeywordsSlurryGeotechnical engineeringBentoniteFly ashGeologyMining engineeringEnvironmental scienceWaste managementEngineeringEnvironmental engineering

Abstract

fetched live from OpenAlex

TVA recently capped a coal ash landfill at the Kingston Power Plant. Cement-bentonite slurry walls were built around the two-mile (three-kilometer) circumference of the facility. The landfill contains roughly 18 million cubic yards (14 million cubic meters) of coal fly ash, including material recovered after the 2008 dike failure at the site. Constructed on the footprint of the failed facility, the new landfill must survive a 2,500-year seismic event. The subsurface, perimeter retaining wall system was designed to stabilize the landfill slopes and contain the stored ash in an earthquake that triggers soil liquefaction. The wall layout consists of evenly spaced shear walls, oriented perpendicular to the landfill perimeter, plus circumferential walls in critical segments. Stantec designed the walls using complex, 2D dynamic computer simulations and 3D structural stress analyses. Deep mixing methods were assumed in the bid package, but prospective contractors were encouraged to propose alternate construction technologies. The winning contractor (Geo-Con, now Geo-Solutions) successfully built the walls using cement-bentonite, slurry trench methods. Over 11 miles (18 kilometers) of wall were constructed, requiring over 520,000 cubic yards (400,000 cubic meters) of slurry. Challenges during construction included characterization of achieved wall strength, mitigation of soil inclusions in the slurry walls, treatment of cold construction joints, soft working conditions on top of the old ash deposits, and collapse of trenches in areas with high groundwater levels.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.367

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 head0.016
GPT teacher head0.232
Teacher spread0.215 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueGrouting 2017Same topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207