Multi Attributed Selection of Excavation Methods in Tunneling Construction
Bibliographic record
Abstract
Abstract \nTunneling construction operations are considered dynamic and complex processes. The success of tunneling construction projects is affected by essential factors. Various methods have been developed in the past, such as simulation models that facilitate and improve the tunneling construction processes and consequently mitigate potential time and cost overruns. Proper selection of excavation method is among the most important factors in tunneling project success. Current practices in selecting the best possible technique for excavation are based on the highest productivity rate as well as the availability of resources. Many of these techniques neglect the interdependencies and concurrency of influencing factors in selecting the best possible excavation method. \nIn this research, a new method for selecting the most efficient excavation method for tunnels is developed. The method considers a series of significant factors in tunneling construction - namely length, cross-sectional area, geotechnical characteristics depth of the tunnel, and level of water table - and a variety of excavation methods, such as different types of TBMs, Road-header, and drilling and blasting. The factors used in the developed method were constructed based on knowledge extracted from literature and gained from interviews with experts and an online survey. The survey gathered data on the relative importance of the set of selection factors for different soil and project conditions. The collected information was used as input to a developed MCA model (AHP, TOPSIS) that ranked the methods based on their respective suitability for the project at hand to select the most appropriate equipment. The developed method is applied to real case studies to demonstrate its use and highlight its essential features. Also a sensitivity analysis was carried out on each of the case studies, to identify and analyze the most sensitive tunneling variables affecting equipment selection. Based on sensitivity analysis geotechnical condition is the most sensitive factors among all effective variables in both case studies. In the Montreal-Laval Metro Extension, the selected method was Road Header, and in the Spadina Subway project EPB Mixed Shield was selected as the most favorable method of excavation. These results confirm the actually selected excavation methods on the two projects, and indicate that the developed method is reliable.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".