Environmental Impact Assessment of Road Asphalt Pavements
Bibliographic record
Abstract
This paper deals with a versatile, synthetic, simple and user-friendly method based on Life Cycle Assessment studies which summarizes multifaceted, often competing, environmental, technical and economic aspects in road construction. In many cases just economic criteria are applied in call for tenders, because the calculation of the environmental impact of road construction is difficult. In fact, it can be referred to many available options and both the economic and the environmental suitabilities have to be considered, in order to achieve globally sustainable results about road infrastructure work. In this research, the weighted sum model of multicriteria analysis is identified as the tool to evaluate global impact of road works, to compare solutions and to choose the best one. The advantages of the proposed approach are that the local contest and the stakeholders’ objective are represented by adopting variable parameters and weights, in order to apply the method to several contexts. A case study explains potential environmental implications of using this new Road Environmental Impact Assessment to calculate effect related to the production of asphalt pavement, considering the production system for aggregates from cradle to gate, the materials transportation to road site and the works to have the road done.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".