A hybrid simulation approach for microtunneling construction planning
Bibliographic record
Abstract
Purpose Construction planning for microtunneling projects is a complex process due to the high level of uncertainties inherent in underground construction and the interdependent nature of decision variables. Simulation is a suitable decision-making tool to account for uncertainties and to model complex dependencies among decision variables. This paper aims to improve microtunneling construction planning by using simulation. Design/methodology/approach This study proposes a hybrid simulation approach that combines discrete event simulation (DES) with continuous simulation (CS) for microtunneling construction planning. In this approach, DES is used to model construction processes at the activity level and CS is used to model the continuous flow of soil material in the system. Findings To demonstrate the capability of the proposed approach in construction planning of microtunneling projects, different construction plan scenarios are compared in a microtunneling case study. The results of the case study show suitability of the hybrid DES-CS approach in simulating microtunneling construction processes and the practicality of the approach for identifying the most efficient construction plan. Originality/value This study proposes a new modeling approach for microtunneling construction processes using hybrid simulation and provides decision support at the construction planning stage of projects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".