Ευφυής παρακολούθηση δομικής κατάστασης λιμενικών έργων ανωδομής με εφαρμογή μη καταστρεπτικών μη επανδρωμένων επιθεωρήσεων

Τσάιμου, Χριστίνα; Tsaimou, Christina

dc.contributor.author	Τσάιμου, Χριστίνα
dc.contributor.author	Tsaimou, Christina
dc.date.accessioned	2025-09-22T09:55:21Z
dc.date.available	2025-09-22T09:55:21Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/62525
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.30221
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	λιμενικά έργα ανωδομής	el
dc.subject	παρακολούθηση δομικής κατάστασης	el
dc.subject	μη καταστρεπτικές μέθοδοι	el
dc.subject	μη επανδρωμένα αεροσκάφη	el
dc.subject	αυτοματοποίηση ανίχνευσης φθορών	el
dc.subject	port waterfront infrastructure	en
dc.subject	structural health monitoring	en
dc.subject	non destructive testing	en
dc.subject	unmanned aerial vehicles	en
dc.subject	automation in defect detection	en
dc.title	Ευφυής παρακολούθηση δομικής κατάστασης λιμενικών έργων ανωδομής με εφαρμογή μη καταστρεπτικών μη επανδρωμένων επιθεωρήσεων	el
dc.title	Intelligent structural health monitoring of port waterfront infrastructure with unmanned aerial vehicle-driven non-destructive inspections	en
dc.contributor.department	Εργαστήριο Λιμενικών Έργων	el
heal.type	doctoralThesis
heal.classification	Επιστήμη Μηχανικού	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2025-07-30
heal.abstract	Ports serve as critical nodes in the global trade network, facilitating maritime connectivity, transportation, and economic growth while supporting the blue economy. As the digital era progresses, ports are increasingly driven to remain competitive across various domains, including infrastructure management and maintenance. Although intelligent technologies are being progressively integrated into numerous aspects of port operations, such as logistics, environmental monitoring, and security, their application in structural performance assessment remains limited. The structural health of port facilities, including berthing structures and rubble mound breakwaters, is vital to ensuring long-term operational functionality. These assets face persistent threats from climate change, natural hazards, marine exposure, and continuous operational loads, all of which accelerate deterioration and compromise structural integrity. Addressing these challenges necessitates transitioning toward digital modeling and advanced condition assessments supported by Structural Health Monitoring (SHM) applications. Enhancing traditional SHM practices with intelligent inspections and automated performance evaluation can assist ports in optimizing maintenance costs, improving risk mitigation strategies, and strengthening resilience planning. Intelligent SHM represents a data-driven evolution of conventional monitoring practices, incorporating sensors, advanced tools, and automation technologies, shifting from reactive maintenance to proactive asset management. This PhD thesis aims to establish a robust and scalable intelligent SHM roadmap for port waterfront infrastructure. The framework integrates Non-Destructive Testing (NDT) with Unmanned Aerial Vehicles (UAVs), computer vision, programming modules, and Geographic Information Systems (GIS) to create an efficient performance assessment methodology capable of addressing the complexities of multi-structure inspections and advancing damage detection automation. The PhD thesis initially contextualizes UAV strengths and limitations compared to conventional SHM methods, building a knowledge base for informed decision-making regarding UAV adoption in port infrastructure monitoring. Following this preliminary evaluation, the thesis investigates the effectiveness of UAV-based inspections in practice. The framework successfully demonstrated the simultaneous monitoring of both concrete pavements of berthing facilities and rubble mound structures, addressing a critical gap in current SHM practices. Periodic inspection campaigns were conducted at Lavrio Port, located on the northeastern tip of Attica, Greece. UAV-based photogrammetry generated geospatial metadata, which was analyzed using GIS tools. The results confirmed that this multi-structure inspection approach minimizes redundant field efforts and provides an informed visualization of the structural condition of port assets. The thesis further advanced the UAV-based SHM framework by incorporating computer vision techniques, programming workflows, and geospatial analytics. For port concrete pavements, specialized programming modules and GIS tools were integrated into a geospatial crack detection methodology. This process accounted for operational challenges, including surface disturbances, shadowing, and environmental variability, significantly improving the accuracy and reliability of condition assessments. Moreover, the UAV-based SHM framework introduced an automated performance assessment for rubble mound structures. UAV-derived photogrammetry datasets were processed using GIS and automated programming workflows to quantify changes in armor layer stability over time. This approach enabled quantified identification of damage evolution and supported the long-term monitoring of maintenance interventions. The structured SHM methodology was validated through an additional in-situ campaign, allowing the final formulation of the intelligent SHM roadmap. During this validation phase, additional challenges were confronted and addressed, demonstrating the framework’s capacity for adaptive improvements guided by engineering judgment. The roadmap’s validation confirmed its reliability, scalability, and adaptability, effectively meeting the demands of modern port infrastructure monitoring. Recognizing that intelligent SHM must bridge data collection with actionable insights, this thesis pioneered the development of structural condition-based vulnerability parameters. The intelligent SHM roadmap was implemented in real-time investigations, where SHM data were integrated into port vulnerability assessments. This novel pathway can enhance risk mitigation strategies and resilience planning, addressing a significant gap in existing port assessment methodologies, which often lack real-time structural monitoring inputs. Overall, the present PhD thesis demonstrates the potential of integrating UAV-driven inspections, geospatial analysis, and advanced automated defect detection into a structured framework tailored for port infrastructure monitoring. The proposed intelligent SHM roadmap offers practical insights and tools that can support ongoing efforts toward enhancing the efficiency and resilience of port maintenance strategies. Future advancements in data-driven structural monitoring can benefit from the overall investigation, paving the way for more automated infrastructure management approaches in complex maritime environments.	en
heal.sponsor	The thesis was supported by the Special Account for Research Funding of the National Technical University of Athens, Greece (Scholarship grant number 65/219100).	en
heal.advisorName	Τσουκαλά, Βασιλική
heal.advisorName	Tsoukala, Vasiliki
heal.committeeMemberName	Tsoukala, Vasiliki
heal.committeeMemberName	Badogiannis, Efstratios
heal.committeeMemberName	Fragiadakis, Michail
heal.committeeMemberName	Chondros, Michail
heal.committeeMemberName	Plati, Christina
heal.committeeMemberName	Karantzalos, Konstantinos
heal.committeeMemberName	Katsardi, Vasiliki
heal.committeeMemberName	Τσουκαλά, Βασιλική
heal.committeeMemberName	Μπαδογιάννης, Ευστράτιος
heal.committeeMemberName	Φραγκιαδάκης, Μιχαήλ
heal.committeeMemberName	Χονδρός, Μιχαήλ
heal.committeeMemberName	Πλατή, Χριστίνα
heal.committeeMemberName	Καράντζαλος, Κωνσταντίνος
heal.committeeMemberName	Κατσαρδή, Βασιλική
heal.academicPublisher	Σχολή Πολιτικών Μηχανικών	el
heal.academicPublisherID	ntua
heal.fullTextAvailability	false