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Record W3214106507 · doi:10.1051/e3sconf/202132501018

Surface and subsurface engineering geological mapping for empirical designs of excavation method and support system of Tunnel 6 of Jakarta-Bandung high speed railway, Indonesia

2021· article· en· W3214106507 on OpenAlexfundno aff
Octavika Malda, I Gde Budi Indrawan

Bibliographic record

VenueE3S Web of Conferences · 2021
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of CanadaCotton Research and Development CorporationChina Railway
KeywordsExcavationRock mass ratingRock mass classificationGeologyGeotechnical engineeringSedimentary rockShotcreteMining engineeringGeochemistry

Abstract

fetched live from OpenAlex

Empirical design of support system at the Tunnel 6 of the Jakarta – Bandung high-speed railway was based on the Basic Quality (BQ) system, which had not been adopted in Indonesia. This research was carried out to better understand the rock mass quality at the tunnel construction site by comparing rock mass quality determined by the BQ system to that determined by two more popular rock mass classifications, namely the Geological Strength Index (GSI) and Rock Mass Rating (RMR). Surface and subsurface engineering geological mapping were carried out and tunnel excavation method and support system were proposed. The engineering geological model of the BQ, GSI, and RMR systems showed that the sedimentary rock masses of the Miocene Jatiluhur Formation generally had poor to very poor quality, while those of the Quaternary Volcanic Formation had very poor to good quality. Based on the RMR, the stand-up time values of the sedimentary rock masses were predicted to be relatively low as compared with those of the Quaternary Volcanic Formation, implying requirement of a relatively quick support system installation after excavation. In general, a combination of systematic bolt, shotcrete and steel ribs is the recommended support system for this tunnel.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.068
GPT teacher head0.277
Teacher spread0.209 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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