MétaCan
Menu
Retour à la cohorte
Enregistrement W7000422891

An Enhanced Mechanistic Analysis Framework for Designing Resilient Airfield Pavements

2022· dissertation· en· W7000422891 sur OpenAlexaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2022
Typedissertation
Langueen
DomaineEngineering
ThématiqueAsphalt Pavement Performance Evaluation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPavement engineeringField (mathematics)RunwayPavement managementRutLoss and damageAdaptation (eye)Software
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Changes in climatic conditions can directly impact pavement performance. With alarming temperature records and the increased frequency of extreme weather events, Canadian infrastructure could be at risk if adaptation strategies are neglected. One such piece of infrastructure, namely airports, are essential to a country`s economic success. The construction and maintenance of long-lasting pavements at such facilities is not only important from an economic standpoint, but for the sake of safety. Therefore, it is crucial that airport infrastructure support safe and efficient transportation. \nIn the past two decades, the usage of mechanistic-empirical design procedures for the design of pavement structures has become more prevalent as compared to empirical methods. The use of such methods enables for the implementation of tools that can account for climate variations in pavement design. The mechanistic-empirical approach relies on predicting pavement responses under traffic and relating these responses to field performance. Pavement stress and strain calculations are necessary to estimate the damage to airport pavements over their service life. To this end, several methods are available, which can vary depending on the computational approach used and the way that material properties are considered. The successful mechanistic analysis of flexible pavements requires appropriate models that can accurately reproduce the pavement behavior. However, choosing the model that will best simulate the pavement responses can be a complex task. \nThis thesis examined some of the available computational approaches used for mechanistic analysis of airfield pavements to summarize the state-of-the-practice and to identify enhancement opportunities. Then, selected software packages were compared with field measurements from two case studies. The first case study compared three full scale pavement sections built for the A380 Pavement Experimental Program (PEP) with linear elastic simulations from KENLAYER, NonPAS and ABAQUS. The second case study analyzed the full-scale results from the National Airport Pavement Test Facility (NAPTF) and compared with non-linear elastic simulations modeled in KENLAYER, NonPAS and GT-PAVE. The outcome indicated that KENLAYER and NonPAS presented good results when predicting pavement vertical strains and stresses at the top of the subgrade in both case studies, however, the vertical displacements predicted in case two were quite far from the field measurements. ABAQUS and GT-PAVE successfully predicted the pavement compressive stresses as well as the vertical displacements. \nThe utilization of a design and structural analysis method that can accurately reproduce the pavement behavior can ultimately improve long-term performance and decrease the frequency of maintenance. Such method can provide realistic assessment of the performance evolution with time and hence allow for more effective and timely pavement management interventions to avoid premature failures. To this end, accounting for environmental factors in airport pavement design remains a challenge since most design methods do not consider inputs such as moisture and temperature variation. Between several airport pavement design methods, only two climatic factors are currently considered in the structural design, i.e., frost depth penetration and the reduction in subgrade bearing capacity due to spring thawing. Therefore, to address this research gap and improve the resilience of airport pavements, this research proposes a new methodology for the structural design of flexible airport pavements, utilizing an enhanced mechanistic-empirical approach that can better accommodate the climate change considerations. \nThe methodology proposed in this research was applied to a case study of Toronto Pearson International Airport, using actual field data. A total of five scenarios were evaluated including (1) the Current Climate, (2) Temperature Increase, (3) Lower Matric Suction, (4) and (5) two Flooding Events. The results of the “Current Climate” showed that the traditional FAARFIELD analysis can possibly overestimate fatigue damage and underestimate rutting damage. \nAmong all climate change scenarios evaluated, fatigue damage was found to be slightly affected by changes in soil saturation, which is present at the “Lower Matric Suction” scenario, and “Flooding Events”. However, the effects of the “Temperature Increase” scenario presented fatigue damages that are 43% higher than the “Current Climate” scenario. From the results of climate change scenarios, it could be recognized that changes in soil saturation have a direct effect in the rutting damage. Both the “Lower Matric Suction” and the “Flooding Events” had great impact in rutting damage, however, the highest damage records happened due to the “Lower Matric Suction” scenario. The lowering of the matric suction due to an increase in the ground water table and precipitation levels affects the soil saturation and lowers the subgrade stiffness. The results showed that a significant decrease in the matric suction in Pearson International Airport could elevate damage in the order of about 117% when compared to the “Current Climate” scenario and decrease the pavement service life down to only a few years. \nConsidering the variations in climatic conditions due to the climate change, the proposed methodology can yield major benefits in terms of quantifying these impacts, which can ultimately help with design of more resilient transportation infrastructure such as airfield pavements. This platform enables accounting for climate variations, temperature increase, as well as extreme events such as flooding in the design of flexible airport pavements. \nThe assessment of an optimum design strategy for airport pavements also incorporated an evaluation of frost and thaw changes due to the temperature rise. The increase in temperature may result in the shortening of the freezing season, which can significantly impact airport pavement frost/thaw conditions. In this thesis, the potential effects of the warming temperature in pavement frost/thaw penetration and frost heave were assessed for critical airports across Canada. To that end, the Ministry of Transportation of Ontario (MTO), Ministère des Transports du Québec (MTQ) and Transport Canada Civil Aviation (TCCA) methods were used in the calculations and climate change simulations considering the emission scenario RCP8.5 in a 20 and 40-year horizon. The results show that climate change predictions result in shallower frost penetration depth and possibly less frost heave over the airports not underlain by permafrost, while airports over permafrost areas might experience an increase in thickness of the active layer. Among the different methods used, the Ministère des Transports du Québec’s (MTQ) had the best performance in predicting frost depth of fine soils, while the frost depth of coarse soils was better estimated by the Ministry of Transportation of Ontario (MTO). \nThis research was the first to propose an enhanced pavement design and analysis framework to improve the resiliency of flexible airfield pavements in face of the changing climate. The proposed framework is unique because it can account for the combined effect of materials properties, loading, and climatic conditions through a detailed analysis of the pavement responses. The implementation of the proposed framework allowed for an assessment of the impacts of temperature increase, lower matric suction, and flooding events in pavement performance in the case study of Toronto Pearson Airport. \nOther key contributions include the study of the impacts of climate and climate change on flexible pavement materials, including the identification of the most relevant parameters for flexible airport pavements. \nThe study of strengths and weaknesses of commonly used methods available to predict pavement responses also provided important contribution, since the influence of using these different tools on the accuracy of the results had not yet been discussed in reference to actual field tests in the existing literature. \nLastly, this research provided a comparative study of the Canadian methods available to calculate frost depth, and its accuracy when compared to field data. The outputs can ease planning of future projects in Canada by facilitating the decision on what methods to use, and how ten (10) major airports in Canada will possibly be affected by the shortening of the freezing season.

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: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,003
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0020,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 GPT0,246
Écart entre enseignants0,233 · 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'étudeSimulation ou modélisation
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

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueUWSpace (University of Waterloo)Même sujetAsphalt Pavement Performance EvaluationTravaux en français237 207