Coastal Geotechnics: An Overview of Applications and Field Work-Based Research with Emphasis on Vertical Pore Pressure Gradients
Notice bibliographique
Résumé
Coastal and Marine Geotechnics are emerging fields offering a geomechanical perspective on coastal processes and shoreline evolution, scour and erosion, coastal and offshore infrastructure design, monitoring, and risk assessment. Geotechnical field measurements and sediment analysis represent an important form of data collection, but are also often challenged by the lack of instrumentation and methods suitable for a wide range of coastal conditions. This presentation has two parts. In the first part, we provide an overview of applications, challenges, and methods for coastal geotechnical site characterization and field measurements. The second part of the presentation focuses on one specific current field of research: vertical pore pressure gradients and their impact on coastal erosion. Understanding vertical pore pressure gradients is challenged by the complexity of sediment-fluid interactions, variability in sediment properties, and in the difficulty of capturing the driving parameters of vertical pore pressure gradients. This poses an issue as large vertical pore pressure gradients can weaken sediment to the point of liquefaction. The increased mobility of the sediment from liquefaction often coincides with a non-zero velocity in the cross-shore direction, possibly resulting in higher rates of sediment transport than in the absence of vertical pore pressure gradients. In this presentation, we discuss the potential role of vertical pore pressure gradients on scour in inundated areas during Hurricane Michael and take a deeper look at the occurrence of vertical pore pressure gradients. Presenter Bio Nina Stark is an associate professor in the geotechnical engineering program at Virginia Tech. She received her M.S. in Geophysics in 2007 from the University of Muenster, Germany, working with the German Naval Research Institute for Underwater Acoustics and Geophysics on mine burial prediction. In 2011, she received her Ph.D. working on the in-situ geotechnical investigation of sediment remobilization processes at MARUM-Center for Marine and Environmental Sciences at the University of Bremen, Germany. In the framework of her Ph.D., she developed a novel portable free fall penetrometer that enables geotechnical seabed probing under energetic hydrodynamics, and pioneered in-situ measurements of seabed surface hardening and softening associated to active sediment remobilization processes. Nina joined Dalhousie University, Canada, as a postdoctoral fellow in 2012 after continuing some of her Ph.D. work as a postdoctoral fellow at MARUM. She joined Virginia Tech in 2013. Her research is focused on coastal geotechnics, coastal and marine field surveying methods, subaqueous sediment dynamics, beach trafficability, and ocean renewable energy. Nina has co-led teams for the Geotechnical Extreme Events Reconnaissance (GEER) association in response to Hurricanes Harvey and Irma in 2017, and she has received the NSF CAREER award and the ONR Young Investigator award in 2018. Nina has been awarded the Anthony and Catherine Moraco Faculty Fellowship in 2019. Matthew Florence is a Ph.D. student at Virginia Tech and a member of the Coastal and Marine Geotechnics research group. Matthew has an M.Sc. and B.S. in Civil Engineering from Virginia Tech. Mathew’s work often taken him to beaches, rivers, and post-storm sites. He also works with proprietary pressure sensors, as well as developing his own.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,004 |
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 ».