Bibliographic record
Abstract
Selected results and initiatives in modern asphalt pavement research for increasing service life of asphalt pavements under the aspect of sustainability and multifunctional use of roads are summarized. Focus lies on innovative approaches and own experience, jointly elaborated during the last decades within the road engineering/sealing components lab at Empa and both the highway/railways engineering and building materials group at KTH. This includes material concepts and design as well as pavement system and construction aspects from an experimental and modelling point of view. It includes also the application of powerful experimental and computational tools, such as Atomic-Force-Microscopy (AFM), X-Ray-Computer-Tomography (CT), Digital-Imaging-Correlation (DIC) and Discrete-Element-Method (DEM). As for materials, recycling issues and the use of Phase-Change-Materials (PCM) or metallic ingredients for inductive thermal crack healing are addressed. In order to remind that material design must also account for the workability during the process of compaction, the new Compaction-Flow-Test (CFT) developed at KTH is shortly presented. Innovative ideas for structural material composition are also mentioned, such as “artificial aggregates” or “additive manufacturing”, being aware that there is still a long way to go. Regarding pavement systems, ideas for multifunctional road applications are proposed. Focus is also put on special issues, such as construction joints.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".