Life Cycle Assessment of Roadways Using a Pavement Sustainability Tool
Bibliographic record
Abstract
Roadway pavement constitutes a significant element in the infrastructure system supporting the transportation network of developed countries. There is a growing need to quantify and minimize the impact of this infrastructure element on the environment, especially with the emerging green initiatives. The Athena Sustainable Materials Institute and Morrison Hershfield are working on an Infrastructure Sustainable Tool with the objective of developing and facilitating transfer of enabling technology to allow decision-makers and design teams to readily assess highway/pavement design options from life cycle environmental and cost perspectives. The project was sponsored by Environment Canada under the Asia Pacific Partnership (APP) program in keeping with the Ministry’s desire to build, strengthen and maintain Canada’s linkages with the international community on global environmental and sustainable development issues. The immediate focus of the project is on developing tools to facilitate highway/pavement design to minimize life cycle environmental impacts in Canada as well as in other countries. The Impact Estimator for Highways is capable of performing life cycle assessments (LCA) for a variety of road cross-sections each of varying length within a single project. The tool covers the material extraction, production and transportation, initial construction, and scheduled maintenance and rehabilitation of a cross-section of a roadway. The ability to model multiple road cross-sections of varying length will facilitate a more accurate modeling and hence comparison of practical road designs. The paper outlines the framework of the tool and presents a case study in applying the tool in the decision making process.
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 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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".