A simulation-based decision support tool for integrating site layout and construction planning of tunneling projects
Bibliographic record
Abstract
Purpose Integrating construction and site layout planning in mechanized tunnel infrastructure projects is essential due to the mutual impacts of construction planning and site layout decisions. Simulation can incorporate site layout planning and construction planning of tunneling projects in a unified environment. However, simulation adoption by industry practitioners has remained relatively limited due to the special skills required for building and using simulation models. Therefore, this paper aims to create a simple-to-use simulation tool that supports site layout and construction operation planning of tunneling projects. This tool intends to promote the simulation application in site layout planning. Design/methodology/approach The current paper proposes simulation as a decision support tool (DST) to provide an integrated environment for modeling tunnel construction operations, site layout and capturing the mutual impacts. A special purpose simulation (SPS) tool was customized and developed for typical mechanized tunneling projects, by tunnel boring machines, to facilitate building the model and allow access to users with limited simulation knowledge. Findings The results show that the developed SPS tool is of great assistance to construction industry practitioners to analyze a variety of site layout and construction plan scenarios and make informed decisions based on its comprehensive and intuitive outputs. Originality/value The main contribution of this research is to promote simulation application in site layout planning of tunneling projects through the development of a simple-to-use tool, which has sufficient details for site layout planning and constraints. The developed DST enables planners to make decisions simultaneously on the site layout, other construction planning variables and identify the most efficient plan.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".