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Using Multichannel Analysis of Surface Waves Method To Evaluate Small-Strain Stiffness of a Geogrid-Stabilised Base

2023· dissertation· en· W7044126226 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Langueen
DomaineEngineering
ThématiqueGeotechnical Engineering and Soil Stabilization
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRutSubgradeAggregate (composite)StiffnessShear (geology)Dispersion (optics)ChannelizedAsphaltShear modulus
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Transportation agencies utilize geosynthetic stabilisation to increase the traffic loading performance and/or reduce the required aggregate layer thickness in roadways. Geosynthetic-stabilised aggregates are often evaluated by researchers and transportation agencies using performance testing. Performance testing assesses the composite behavior of the selected aggregate and stabilising geosynthetic; however, the results are limited to the selected aggregate and subgrade soils, geosynthetics, and traffic loading conditions. To continue expanding upon the current database on geosynthetic stabilisation, an accelerated traffic loading device, referred to as the full-scale wheel trafficker system (FSWTS), was developed at the University of Saskatchewan. Two types of aggregate were evaluated with four different variable aperture shaped geogrids (VASGs): a local prairie aggregate, and a high-quality, imported crushed rock aggregate. Channelized traffic loading was applied pneumatically to each test section, and the rut depth was measured intermediately to determine the magnitude and rate of permanent deformation. Using multichannel analysis of surface waves (MASW), the shear wave velocity (Vs) and small-strain shear modulus (Gmax) were also measured through the aggregate. The MASW results were used to determine the traffic-induced changes in stiffness in each section. The average Vs was measured through the aggregate (in the wheel path) after short-term and long-term traffic loading using dispersion analysis. It was found that geogrid-stabilisation reduced the time-dependent degradation of stiffness (i.e., Vs) most effectively in the finer, less fractured prairie aggregate; however, the rutting performance was comparable amongst the test sections. In the high-quality crushed rock aggregate, the test section stiffness degradation and rutting performance was comparable for the geogrid-stabilised and non-stabilised test sections in the short term; however, there is some stiffness enhancement observed in the long term for one of the geogrid-stabilised test sections. A third trial was completed in the FSWTS with the CR aggregate, which altered the lane locations of the VASGs and control section from the previous trial. Profiles of Gmax with depth were measured (in the wheel path and outside the wheel path) through the CR aggregate after short-term and long-term traffic loading using inversion analysis. The stiffness degradation measured in the wheel path aligned with the rutting performance and Shakedown theory. The geogrid-stabilised test sections resulted in less stiffness degradation than in the control section, both in the wheel path and outside the wheel path. The aggregate stiffness outside the wheel path was most effected by loosening and upheaval of the aggregate, which was most prevalent in the control sections. Geogrid-stabilisation is effective in reducing the traffic-induced degradation of stiffness in unsurfaced roadways. MASW has also been proven a feasible option for shallow subsurface analysis of road structure materials. Additional trials should be completed in the FSWTS to further refine the code for MASW, and to contribute more geosynthetic performance testing data to the current database. The dispersion analysis program should also be further refined to study higher frequencies through the aggregate; thus, better capturing the effective confinement thickness across a geogrid-stabilised aggregate. Ideally, MASW can be utilized to study both the aggregate and subgrade stiffness in future trials.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
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: Empirique
Score de désaccord entre enseignants0,131
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,020
Tête enseignante GPT0,219
Écart entre enseignants0,199 · 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 tête enseignante, pas un consensus.

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
Publié2023
Routes d'admission1
Résumé présentoui

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