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Record W2932959222 · doi:10.11159/icgre19.143

Efficiency of Biocementation as Rock Joints Sealing Technique Evaluated Through Permeability Changes

2019· article· en· W2932959222 on OpenAlexvenueno aff
Emad Arbabzadeh, Rafaela Cardoso

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
FundersInstituto Superior TécnicoFundação para a Ciência e a TecnologiaUniversidade de Lisboa
KeywordsPermeability (electromagnetism)Materials scienceComposite materialChemistry

Abstract

fetched live from OpenAlex

In Microbially Induced Carbonate Precipitation (MICP), bacteria are used to hydrolyze urea. In the presence of a calcium source supplied in a feeding solution, calcium carbonate is formed and precipitates. MICP has shown promising results in terms of improving the hydro-mechanical properties of sandy soils by forming bonds connecting the particles. Recent studies are focused on using MICP for sealing discontinuities, such as concrete and stone cracks, and rock joints. This is investigated in this paper for a diskshaped rock sample having a crack along the entire diameter. In the study presented, enzyme is used instead of bacteria, because prior studies proved that production of calcium carbonate is faster while using enzyme. In addition, large quantities of enzyme required for Civil Engineering applications can be produced easier comparing to bacteria, and for this reason, using enzyme may be an alternative to using bacteria. The efficiency of the method was evaluated by constant head water permeability test during the treatment. The permeability of the crack reduced along time and the crack was almost completely sealed after 6 hours of treatment. Upon completion of the treatment, the crack was investigated visually to detect the presence of precipitated biocement.

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 score0.005
Threshold uncertainty score1.000

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)0.0010.000

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 head0.007
GPT teacher head0.221
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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicMicrobial Applications in Construction MaterialsFrench-language works237,207