Research on Quality Improvement and Safety Measures of Highway Pavement Construction by Unmanned Aircraft Swarm Operation Based on Optimal Control Theory
Why this work is in the frame
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Bibliographic record
Abstract
Aiming at the traditional pavement construction, there are problems such as poor construction conditions, limited quality inspection methods, backward control mode and incomplete management means.In this environment, the project in this paper (Gansu Road and Bridge Anlin Pavement Second Standard Project) uses multi-objective particle swarm optimization algorithm to establish a multiobjective machine group optimization configuration model based on quality constraints under the schedule -cost, and the first time to quote asphalt pavement to carry out the intelligent construction of unmanned machine group in Gansu Province.Analyze the intelligent unmanned machine group composed of auto-pilot paving technology and roller auto-pilot technology.Design the optimal configuration model of highway construction machine group, and use multi-objective particle swarm algorithm to design the cooperative operation of unmanned machine group.Combined with the optimal configuration of highway construction fleet problem itself, the standard particle swarm algorithm and fleet configuration model are also modified and improved.Simulate the highway pavement construction process, emphasizing the preparation of construction personnel, machinery, and management platform.The parameters of particle swarm algorithm are designed to solve the optimal construction machine fleet optimization configuration under quality constraints of durationcost.The machine utilization and duration of scheme 2 are 15.23% and 10.96%, respectively.With the priority of duration, scheme 2 is selected as the machine fleet configuration scheme.Option 4 has the lowest machinery cost of 9.41%.With the priority to ensure the maximum profit, option 4 can be chosen as the machine swarm configuration scheme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it