Energy savings in transportation: Setting up an innovative SHM method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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