Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/33150
DC FieldValueLanguage
dc.contributor.advisorMarkou, George-
dc.contributor.authorBraun, Kevin-
dc.date.accessioned2024-11-06T06:52:54Z-
dc.date.available2024-11-06T06:52:54Z-
dc.date.issued2027-12-01-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/33150-
dc.description.abstractWind turbine foundations are critical components of renewable energy infrastructure, designed to support substantial dynamic loads and withstand environmental conditions throughout their operational life. While wind turbine superstructures typically have an operational life of 20 years, the reusability of the foundation beyond this period remains largely unexplored. This research addresses the ageing and degradation of wind turbine foundations, focusing on developing predictive models to assess their long-term structural integrity and potential for reuse. Utilizing advanced numerical modelling techniques, structural health monitoring (SHM) and material property validation, this study will generate a dataset to determine the ultimate limit state (ULS) capacity of wind turbine foundations at various stages of ageing. This dataset will be used to train machine learning algorithms, resulting in developing predictive models that can inform decision-making on the reusability of ageing foundations for new wind turbines. The project also aims to validate models for concrete ageing and degradation with real-world data from in-situ structures, enabling realistic assessments of foundation performance over time. Additionally, the research will explore innovative foundation designs, retrofitting strategies, and strengthening techniques to extend foundation life, thereby contributing to a more sustainable energy infrastructure. The outcomes will provide valuable insights into foundation reusability, SHM integration, lifespan optimization, and cost-effective design strategies for the renewable energy sector.en_US
dc.rightsAttribution-NoDerivatives 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectWind Turbine Structuresen_US
dc.subjectMachine Learningen_US
dc.subjectSoil Structure Interactionen_US
dc.subjectNonlinear Modelingen_US
dc.subjectFoundationsen_US
dc.subjectAssessment of Structuresen_US
dc.subjectReusability of Foundationsen_US
dc.subjectRenewable Energyen_US
dc.titleDevelopment of predictive models for the assessment of the wind turbine foundation's lifespan and structural integrity using ML algorithmsen_US
dc.typePhD Thesisen_US
dc.affiliationUniversity of Pretoriaen_US
dc.relation.deptCivil Engineeringen_US
dc.description.statusCurrenten_US
cut.common.academicyearemptyen_US
dc.relation.facultyEBITen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_db06-
item.openairetypedoctoralThesis-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-6891-7064-
crisitem.author.orcid0000-0002-6891-7064-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Διδακτορικές Διατριβές/ PhD Theses
CORE Recommender
Show simple item record

Page view(s)

42
Last Week
33
Last month
checked on Nov 22, 2024

Google ScholarTM

Check


This item is licensed under a Creative Commons License Creative Commons