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Record W2525957594 · doi:10.11159/mmme16.113

Analysis of the EPB-TBM Excavation Parameters Used in a Tunnel Construction in Istanbul

2016· article· en· W2525957594 on OpenAlexvenueno aff
Omur Acaroglu Ergun, Cemalettin Erdoğan, Emre Ekinci

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsExcavationMining engineeringGeotechnical engineeringGeologyCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Mechanical excavation methods have become widespread in the world and our country and importance of the selection of proper machine is getting increase for achieving efficient excavation. In this study, the excavation parameters of an EPB-TBM machine used in tunnel project which is purpose of collecting the waste water from Eyp to Yenikap in the ISKI Yenikap Waste Water Plant were analyzed. During the machine passing under a river bed in the Gngren formation, seven ground samples were collected and sieve analyses were made. The ground of continuing, particle size distribution and ground conditioning type were examined by using sieve analysis. After the Gngren formation EPB-TBM was entered in the Trakya Formation and started excavation fractured and jointed claystone. During the excavation in this relatively harder formation, the machine was transformed from EPB to TBM mode, and then some of the wedge cutters were placed with disc cutters for increasing the efficiency of the excavation. During this period, analysis of the excavation parameters were made using the torque, thrust force and those penetration indexes, advance and penetration values rate. This study shows that data obtained from machine gives useful information about formation passing.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicTunneling and Rock MechanicsFrench-language works237,207