Integrating smart city technologies for sustainable pavement infrastructure
Bibliographic record
Abstract
This article explores the connection between Smart City advancements and the development of materials and strategies for asphalt pavements. It highlights the synergy between Smart Cities and Industry 4.0 through current technological advancements using connectivity, artificial intelligence, and big data analysis. The relationships between Smart Cities and Industry 4.0 are examined with an emphasis on five major areas: (i) Internet of Things and integrated systems; (ii) robotics and additive manufacturing; (iii) augmented reality for modeling and simulations; (iv) data-driven analysis: big data, artificial intelligence; and (v) citizen participation. We also point to the importance of smart infrastructure, with the integration of nanomaterials and materials into the structure to reduce energy consumption, optimize resources, and assist automation in construction and maintenance strategies, contributing to the efficiency and longevity of pavement infrastructure. We conclude by envisioning how generative artificial intelligence is integrated into pavement research, opening new avenues for innovation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".