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Enregistrement W3160211093

IMPACT OF FREEZE-THAW AND DE-ICER ON THE STRUCTURAL AND FUNCTIONAL PERFORMANCE OF CANADIAN AIRPORT ASPHALT MATERIALS

2021· dissertation· en· W3160211093 sur OpenAlexaboutno aff
Y. Liu

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2021
Typedissertation
Langueen
DomaineEngineering
ThématiqueAsphalt Pavement Performance Evaluation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAsphaltEngineeringAsphalt pavementCivil engineeringForensic engineeringTransport engineeringEnvironmental scienceGeographyCartography
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Asphalt pavement is widely used on roadways and airside pavement across Canada. Despite various benefits including smooth driving experience, rapid construction, as well as the high recyclability of asphalt pavement, common failures such as permanent deformation on the heavy traffic-dominated road and shear-related, distresses on airport pavement have drawn the attention of researchers and pavement engineers. During hot summer, slow-moving heavy trucks during rush hours can generate permanent deformation and shorten the pavement service life. During the long harsh winter, big diurnal temperature variations make asphalt material experience numerous freeze-thaw cycles. Various anti-icing and de-icing agent are often applied on roads and airport pavements to improve safety during these hazardous weather conditions. However, these ice control chemicals can also potentially impact the asphalt pavement in the long term. \nCompared with roadway pavement, the loading conditions on airport pavement are often less frequent but much higher in terms of loading. Under various aircraft ground maneuverings such as taxi, landing, and takeoff operation, airside pavement bears not only enormous vertical loading but also a significant horizontal force generated between gear tires and pavement surface when turning the aircraft. \nThree typical Ontario road-used asphalt mixtures were tested with the Hamburg Wheel Tracking Test to investigate the effect of different mix design parameters on the permanent deformation resistance. Industrial X-Ray CT and digital image process technology were employed to explore the morphological properties of processed two-dimensional images and their relations with the permanent deformation of the corresponding mixtures. Typical Road asphalt mixture along with airport asphalt mixture was exposed with Freeze-thaw cycle and de-icing agent, namely potassium acetate to investigate the effect of those factors would have on extreme weather conditions. The de-icing chemicals were also evaluated to examine permanent deformation and shear resistance performance through Hamburg Wheel Tracking Test and Simplified Uniaxial Shear Test. It was found that the asphalt binder PG level has a significant impact on the permanent deformation performance of roadways mixtures, the passing rate of the 4.75 mm sieve plays an important role in rutting resistance. The rutting depth and morphological indexes are not well-correlated, the possible explanations include whether the rut test can represent the actual stiffness of the corresponding asphalt mixture, the binary process might misrecognize the small particles and voids. 50% Potassium acetate liquid seems to have the softening effect on asphalt mixture’s overall stiffness for both airport and roadway mixes. De-icing treatment and freeze-thaw cycles have the potentials to induce stripping. Simplified Uniaxial Shear Tester (UST) is capable of evaluating the shear resistance of asphalt mixture. Mix type and treatments to the specimen have a significant impact on the shear performance of the asphalt mixture. \nA three-dimensional Finite Element model was built based on the pavement structure of taxiways in Toronto Pearson International Airport using ABAQUS, both vertical and horizontal loads were applied where nose gear and main gear contact with the pavement surface. Linear viscoelasticity was considered for engineering properties of asphalt layers. Various ground maneuverings such as landing, taxi, takeoff, zero fuel, and operating empty were simulated in the model to analyze their respective impact on shear stress and displacement distributions within the pavement structure. The results show that Shear Stress S12 generated by main gear loads is 5 times greater than that generated by nose gear on asphalt surface; Shear stress S13 generated by main gear loads is equal to that by nose gear but with an opposite direction; Shear stress S23 generated by main gear loads is almost 16 times greater than that by nose gear loads on asphalt surface layer. For shear stress S12, taxi creates the greatest level while the operating empty the lowest. For shear stresses S13 and S23 however, takeoff generates the highest stress level while empty operating generates the lowest.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,724
Score d'incertitude au seuil0,549

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,199
Écart entre enseignants0,187 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

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