Maintenance costs of public roads: Do empirical data confirm the superiority of concrete over asphalt?
Bibliographic record
Abstract
One of the cornerstones of far-sighted infrastructure management is that it involves a life-cycle consideration, as endorsed notably by the U.S. Federal Highway Administration. Decisions on investments should be considered in terms of product performance over time. In spite of the initial construction cost, all constituents of maintenance (i.e. cost to be borne during road’s assumed lifespan to keep the specified service level) should be quantified to equip the public investor with full information on pavement’s long-run prospects. The major part of the existing research in this field rests on theoretical calculations which, although sound and scientifically rigid, may not always translate accurately to the actual wear and tear of pavements. Maintenance costs are also delivered to the broad public by tools like Canadian CANPav™ or Polish Kalkulator drogowy, none of which is faultless. This paper aims to apply the real life statistics on concrete versus asphalt construction costs, assembled from the commune of Grybów in southern Poland. Despite limited time series, it is still instructive to cast a closer glance at these few actual figures instead of proving concrete pavements’ long-term advantage on the basis of theoretical or anecdotal evidence. Moreover, the paper addresses the underestimated issue of local concrete roads that often gives way to more rewarding research on high-traffic-volume concrete pavements. The analysis revealed that for a 30-year horizon, given very conservative assumptions as to the discount rate, inflation rate, and the scope and frequency of pavement rehabilitation, concrete pavements are still more economical relative to bituminous pavements. For a representative road section, the net present value of future cash flows related to its maintenance was 5.67% lower in PCC concrete technology. The life-cycle saving may go up to as much as 33-35% when excessively cautious assumptions are lifted.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".