The Impact of Portland Cement on the Performance of Cold In-Place Recycled Pavement
Notice bibliographique
Résumé
In place recycling of existing asphalt pavement is a rehabilitation strategy, which the Ministry of \nTransportation Ontario (MTO) adopted decades ago. Asphalt emulsion is the most common type \nof bitumen stabilizing agent incorporated into recycled mixtures in Ontario. Active fillers such as \nPortland cement, hydrated lime and fly ash can be added to a volume fraction of less than 1% to \nthe recycled mixtures to improve dispersion of the bitumen in the mixtures and to increase the \nstiffness of the mixtures and the rate of strength gain. This study is a project of the Centre for \nPavement and Transportation Technology (CPATT) laboratory at the University of Waterloo in \npartnership with the MTO and various industry partners in Ontario. Cationic Slow Setting \nEmulsion (CSS-1H) and Anionic High Float Emulsion (HF-150) emulsions were mixed with \ntypical Reclaimed Asphalt Pavement materials (RAP), which are pulverized (HL8 and HL3) in the \nCPATT laboratory at the University of Waterloo. To improve the dispersion of the bitumen in the \nmixes and to increase the stiffness of the mixtures and the rate of strength gain, different \npercentages of Portland cement (0%, 0.5%, 1.5% and 3%) were added to the mixes. Four mixes \nwere prepared. Mixture 1 (M1) was developed using the cationic slow setting emulsion-CSS-1H \nthat was mixed with a pulverized HL8 RAP material; for the second mixture (M2), the Anionic \nHigh Float Emulsion (HF-150) was mixed with a pulverized HL8 RAP material. However, for the \nthird mixture (M3), the cationic slow setting emulsion-CSS-1H was mixed with pulverized HL3 \nRAP material, and for the fourth mixture (M4), the Anionic High Float Emulsion (HF-150) was \nmixed with pulverized HL3 RAP material. These mixes were tested and evaluated using Indirect \nTensile, Dynamic Modulus, Fatigue and Thermal Stress Restrained Specimen Tests. Furthermore, \nthe moisture susceptibility was controlled by conditioning the specimens for 24 hours at 25 degrees \nCelsius before testing. The results showed that increasing the percentage of Portland cement led \nto an increase in strength. For the Cold In-Place Recycled Asphalt (CIR) mixes, the addition of \nPortland cement increased the stiffness. For M1 and M2 mixes, there was a tradeoff relationship \nbetween the stiffness and fatigue life. Adding amounts of Portland cement between 0.5% and 1.5% \nto these mixes increased the stiffness significantly with only a small reduction in fatigue life. As \nPortland cement was added to M3 and M4 mixtures, the stiffness increased until the cement content \nreached 1.5%. Additions above 1.5% did not produce a noticeable increase in stiffness. Also, the \ntensile strength and moisture resistance improved significantly with the addition of Portland \nv \ncement. However, the low-temperature fracture properties of the mixtures deteriorated due to \nincreased brittleness and the development of shrinkage cracks that caused the mixtures to fail at \nhigher temperatures and lower fracture stresses. In all cases, adding amounts of Portland cement \nbetween 0.5% and 1.5% enhanced the stiffness, tensile strength, and moisture \nsusceptibility resistance of the mixes with only a small reduction in fatigue life. Finally, a classic \nMaxwell model, a rheological model comprised of a spring and dashpot in a series of materials, \nwas used to simulate either the stress-strain behaviour of the material or the force-deformation \nrelationship of a test specimen. The model, once calibrated using the stress-strain data from a single \nspecimen fatigue test, accurately described the stress-strain histories for all tests. Features \ndescribed by the model included the stress-strain hysteresis loop shape, the relaxation of mean \nstress to zero under controlled strain cycling and the decrease of stress range with cyclic straining. \nThe latter gave accurate predictions of the reduction of the initial stiffness to one-half its initial \nvalue, which was used as the definition of failure.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».