Analysis of the EPB-TBM Excavation Parameters Used in a Tunnel Construction in Istanbul
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".