Resource Management of Bridge Deck Rehabilitation: Jacques Cartier Bridge Case Study
Bibliographic record
Abstract
The condition and performance of bridges vary widely across North America. The large amount of expenditures on bridges needs significant efforts to optimize budget and resource allocation and to select the best rehabilitation or replacement method, which reduces project cost and duration. Simulation has been widely used in the construction area to optimize productivity and resource allocation. Current research optimizes resource combination for bridge deck rehabilitation projects using discrete event simulation. The Jacques Cartier Bridge redecking project is selected as a case study. Data related to productivity and duration of different activities were collected from the project. Probability distributions are fitted, which show the robustness of normal distribution to fit most variables. A simulation model is developed for this project in order to experiment with and perform sensitivity analysis. Based on the simulation results, an optimum resource combination of deck rehabilitation is obtained, which is [five teams, two saws, three old section trucks, and five new panel trucks] TSON 5235 with the unit (panel) cost of $747∕h (direct cost only). The model developed is tested against real productivity where it shows reasonable results. The present research is relevant to both researchers and practitioners. It provides bridge redecking researchers with a real case study, a simulation model, and an approach to analyze projects. It also provides practitioners with an approach to optimize the usage of their resources considering direct 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".