Risk analysis in paving operations using discrete event simulation: a case study of Taiwan permeable asphalt concrete pavement pilot road project
Bibliographic record
Abstract
The simulation model used in this study is based on a construction project in Taiwan. In this study, STROBOSCOPE and EZStrobe were used as discrete-event simulation software to assess the potential risk of the construction in terms of time and cost. The construction process, the unit cost of the resources, and the operation time of each activity were modelled based on the actual construction procedure. A total of 30 replications was required for each scenario. Scenarios with real-world data inputs and eight other models were simulated using the software. The best scenario with the lowest cost and competitive time was selected as the base model for a sensitivity analysis. During the Sensitivity Analysis, the best model 8-R was generated by changing the number of labourers to 13 and Excavators to 3. Compared to the real-world project, the total costs were reduced by 11% and the total time was reduced by 58%. Moreover, the result of risk analysis based on scenario 8-R demonstrated that using a number of labourers, trucks, excavators, rollers, millers, and pavers less than the quantities that were used in the real-world data, the associated risk in term of cost and time will exceed the real-world project cost.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".