FORMATE DURABILITY OF ROAD PAVEMENTS - LITERATURE SURVEY AND LABORATORY TESTS
Bibliographic record
Abstract
Based on literature, the influences of formate de-icing chemicals into the characteristics of road asphalt are investigated only somewhat in airfield conditions e.g. in Sweden, Norway, Canada and in Finland. Differences between normal road asphalt paving concrete, AC16 in Finland compared to airfield asphalt paving concrete are, however, so small that the results obtained from the airfield conditions are well applied also in road conditions. Wear influences are not existing on airfields like in normal road conditions. That is why the wear of normal asphalt paving mixtures, influenced by potassium formate de-icing chemical, was researched in laboratory. Based on present opinions, formates influenced the quality of asphalt pavements. Formates influenced in different ways road binder, bitumen, aggregates and resistance of adhesion between aggregate and bitumen. Influence in bitumen is determined by softness of bitumen. Potassium formate is penetrating to surface of soft bitumen, B160/220, during the laboratory storage test, so effectively that the bitumen film is separating into the surface of this de-icing chemical. In compacted asphalt pavement the influence in adhesion between aggregate and bitumen seams to be smaller and is due to the tightness of the asphalt pavement. The precise influence mechanism of formate into the bitumen consistency and into the asphalt pavement characteristics has so far, however, been unknown and not well researched. Influences of formates should be prevented by the improved mix-design of the consistency of the road pavements. Influences of formate in road bitumen are decreased with the harder road bitumen. The road bitumen, B160/220 is proved at least to be too soft as the road bitumen with formate chemical. When using potassiumformate the hard road bitumen, B70/100 should be selected. When avoiding cold-cracking in pavements, it should be better to select the polymer-modified bitumen (PmB) with the formates. PmBs are, however, in normal road asphalt use, compared to normal bitumen, more expensive. The preliminary wear tests of road asphalt concretes after 2 months storage in 50% potassium formate water solution were carried out in laboratory. The wear test was the Prall wear test based on standard prEN 12697-16. Road asphalt concrete, AC16 (B70/100) and stone mastic asphalt concrete, SMA16 (B70/100) were worn less than the corresponding samples stored in normal water. Wear results showed that any immediate wear risk with potassium formate was not detected. Influences of the other de-icing chemicals in wear were not researched. Because of the small data in wear results final conclusions regarding wear influence could not be made. In the literature, the abrasions type wear of the acid aggregate, quartzite. by icing and melting cycle wear tests were researched. The wear of quartzite in this abrasion test was greatest when 2% sodium formate water solution was used in storage of the aggregate particles. That is why wear of road pavements and adhesion of bitumen to aggregate should be tested with formates more, also with the Finnish road aggregates. Interpretation of the results obtained from formates in literature was not such that it actually could prevent the use of potassium formate in de-icing on the road surfaces. Both formates and acetates in the literature survey behaved in de-icing of asphalt pavements, additionally, rather equally and better than urea chemicals. Certain criteria, connected to possible use of potassium formate in de-icing on the road surfaces must, however, still be studied more during the mix-design procedure of the road pavements. On the sensitive ground water areas, the selection of potassium formate for de-icing needs still more results on the real degradation of the chemical. When using potassium on the sensitive ground water areas the mix-design of asphalt pavement layer should be done so that the maximum void content is 3% by volume. This improves adhesion between aggregate and bitumen. Formate will then dissipate and evaporate directly from the asphalt concrete surface preventing penetration of formate into the pavement structure. Asphalt pavement structure, during the use of formate, should then be as dense as possible and remain without cold-cracking. The asphalt pavements consist sometimes also of new and old asphalt pavement wearing layers and the acid bitumen emulsion is used as the glueing agent between these layers.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".