Enhancing Real-Time Data Acquisition from Embedded Road Structural Health Monitoring Systems
Bibliographic record
Abstract
Canada's economy heavily relies on its roadways, yet managing pavement assets faces challenges due to past infrastructure spending cuts. Addressing this, a pavement management system (PMS) is essential for efficient resource allocation. Traditional surface condition monitoring within PMS is time-consuming and costly. In response, in situ condition monitoring, integrating AI and ML, emerges as a viable alternative, aligning with the development of "smart" pavements. This thesis, part of the Smart Pavements project at the University of Waterloo, assesses an instrumented pavement section on Courtland Avenue, Kitchener. It provides a comprehensive assessment of the implementation, data collection, data analysis and database concept development processes of an instrumented test section. It integrates advanced monitoring technologies and predictive modeling, yielding promising results. Identified gaps in the literature are addressed, with scalability and cost-benefit analysis highlighted for future research. Challenges in instrumentation and testing, including weather delays, are discussed, with recommendations provided. Material testing procedures and truck testing results are outlined, emphasizing seasonal variations and the impact of vehicle wander on pavement behavior. Software comparisons and detailed trend analysis reveal insights into pavement performance. Additionally, a basic database framework is proposed for efficient data management. This study contributes to understanding pavement instrumentation, long-term behavior, and the efficacy of simulation methods. Recommendations for future work include AI/ML integration, long-term data collection, database development, and standardized guidelines for sensor implementation, aiming to enhance pavement management practices nationwide.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".