Research Trends in Pavement Management during the First Years of the 21st Century: A Bibliometric Analysis during the 2000–2013 Period
Bibliographic record
Abstract
A bibliometric analysis based on Scopus database was carried out to identify the global research trends related to pavement management area from 2000 to 2013, and to improve the understanding of the research topics in that period. The results reveal two stable periods of annual publications, from 2000 to 2002 with an average rate of 27, and from 2003 to 2010 with a value of 51; and a period with an increasing production rate of 20 publications per year after 2010. According to the document-type distribution, articles and conference papers have almost the same contribution. The most productive country was the United States, followed by Canada and China. The research trend in the field of pavement management could be grouped into three main areas. The first one is related to pavement management systems, which attracted the greatest attention, especially optimization processes with various objectives and lifecycle cost analysis. The second group is about pavement performance modeling, where calibration of mechanical empirical models was largely developed. Lastly, data collection had also occupied several papers, mainly about cracking classification. Sustainability aspects in pavement management became an emergent issue. The trending issues in that period, in these categories, were summarized in the paper.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.088 | 0.190 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".