Energy savings in transportation: Setting up an innovative SHM method
Bibliographic record
Abstract
Transportation systems are gradually changing.Innovative solutions are provided by automotive industries, construction firms, computer-aided pavement management systems, sensor-based structural health monitoring (SHM) systems, and international regulations.This calls for efforts and studies aiming at finding a trade-off between the ever-growing request of innovation (smart cities), and the never-ending depletion of resources and energy.Energy savings in road infrastructures can be pursued through: 1) construction process optimization; 2) traffic management improvement; 3) vehicle optimization; 4) recycling and reuse of construction materials; 5) innovative materials; 6) energy harvesting; 7) smart roads; 8) maintenance and rehabilitation optimization through SHM methods and technologies.The objective of the study is to set up an innovative SHM method aiming at achieving energy savings in transportation in terms of Pavement Management System (PMS) optimization.The new method here setup was implemented through an experimental investigation.A microphone was placed on different road pavements, impulse loads were produced by a Light weight Deflectometer, LWD, and vibro-acoustic signals were recorded and analyzed in the pursuit of assessing the structural condition of the pavement.Using this knowledge to improve the management process of transportation infrastructures, it is expected that safer, more resilient, and less energy-consuming assets will be provided.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".