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an ARC Linkage Project focused on developing an autonomous system for detecting and quantifying structural damage in infrastructures (e.g., bridges, grain silos) using computer vision, digital twins, and
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, meteorological and physical conditions they operate under. Such data can inform structural health monitoring for offshore wind turbines or help plan new offshore sites, via estimation of power yield in relation
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learning system that creates a profile of wear and tear of turbines given the environmental, meteorological and physical conditions they operate under. Such data can inform structural health monitoring
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techniques. This research proposes a novel framework that integrates Machine Learning (ML) for structural health monitoring (SHM) and design optimization of CFDST wind turbine towers. The study will focus
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, C., Tcherniak, D. (2022). On Explicit and Implicit Procedures to Mitigate Environmental and Operational Variabilities in Data-Driven Structural Health Monitoring. In: Cury, A., Ribeiro, D., Ubertini
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on the development of self-sensing fiber-reinforced cementitious composites (SS-FRCMs) for the dual purpose of structural strengthening and real-time health monitoring of infrastructure. The research is structured
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(£19,237 for 2024/25). The proposed project addresses significant health and safety concerns in the UK construction sector, where workers are more prone to suffer with emotional stress, mental tiredness, and
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to determine the molecular structure and function of a TZ sub-complex consisting of 3-5 proteins. You will monitor the gating mechanism of TZ in cellular models such as RPE1 or cultured dopaminergic neurons by
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the area of structural health monitoring of civil engineering structures on an Australian Research Council Future Fellowship project “Innovative Data Driven Techniques for Structural Condition Monitoring
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to a wide range of engineering problems, including real-time structural health monitoring, vibration analysis, and control design. The ideal candidate will have an outstanding engineering or related