Special purpose simulation templates for tunnel construction operations
Bibliographic record
Abstract
Simulation is a powerful tool for decision making. It provides an appealing approach to analyze and improve repetitive processes such as tunnelling. Notwithstanding this appeal, application of simulation to real-life construction projects has been minimal. This paper describes the design, development, and application of a special purpose simulation tool for actual tunnel construction operations performed by the City of Edmonton Public Works Department. The implementation of this tool in industry was successful and serves as a model for others to follow. The decision-making process adopted by the model developers and the construction industry personnel during the design, development, and implementation of the simulation are described. The cost-planning tool in the tunnel template is very useful in making decisions and evaluating the feasibility of tunnel construction projects. The real-life application of various alternatives compared to the conceptual estimates prepared for a proposed tunnel project to be constructed in Edmonton is presented in three stages. The basic costs, operational costs, support costs, productivity, duration, and resources utilization data are presented for different alternatives for the proposed tunnel project. Future modifications required by the engineering staff of the City of Edmonton, and the proposed research for modelling uncertainties in tunnel construction are identified. The successful application of the simulation for actual construction project highlights the interactive collaborative research work between academia and industry.Key words: simulation, tunnelling, construction, modelling, planning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".