Optimizing the inspection schedule for bridge networks
Bibliographic record
Abstract
Optimizing the inspection route for a bridge network is a paramount factor in reducing inspection duration and cost, particularly for bridges located in a large geographical area. In this respect, this study presents a bridge network inspection planning model. The model aims to minimize the total inspection cost by reducing traveling distance, accommodation cost, wasted time, and inspection crew cost. The model was developed based on the main concept of the multiple traveling salesman problem. In this model, a discrete event simulation engine was built to estimate the inspection duration for each bridge in the network, whereas the genetic algorithm approach was used to optimize the inspection route. Python was used in coding the model steps. Web scraping technique was utilized to develop a geospatial information algorithm dedicated to extracting the actual driving distance between any two points in the inspection route. To tackle the limitations of previous models, the developed model considered several parameters, such as bridge inspection durations that are either shorter or longer than a day shift, variations in the accommodation fees, actual driving distance, minimum workload to assign a new inspection crew, and minimum time to allow starting inspection activities in a new bridge. Considering these parameters make the developed model comprehensive and more accurate. The model was tested against a real bridge network in the IL, USA, and it proved its effectiveness in optimizing the inspection route. The model provides strong guidance for consultants and authorities in charge of bridges in planning the upcoming inspection activities.
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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 | 0.001 |
| 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 | 0.000 |
| 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".