Towards autonomous structural safety prognostics: integrating in-situ imaging and predictive modelling. This project aims to advance a scientific basis for autonomous safety prognostics by developing predictive models and in-situ damage imaging principles. Development of this new health prognostic approach will overcome the significant challenge of safety assurance of composite structures in the presence of in-service damage, which is largely hidden.
Baseline-free Methods for Early Damage Diagnosis using Nonlinear Ultrasound. To address the significant limitation of existing non-destructive evaluation techniques in detecting and characterising early damage, this project aims to discover the physical nature of self-generated nonlinear waves by structural damage and to explore its potential for an entirely new class of non-destructive evaluation and structural health monitoring techniques. Major applications are expected to include a baseline- ....Baseline-free Methods for Early Damage Diagnosis using Nonlinear Ultrasound. To address the significant limitation of existing non-destructive evaluation techniques in detecting and characterising early damage, this project aims to discover the physical nature of self-generated nonlinear waves by structural damage and to explore its potential for an entirely new class of non-destructive evaluation and structural health monitoring techniques. Major applications are expected to include a baseline-free structural health monitoring technique capable of detecting and quantifying barely-visible impact damage in advanced composite materials, non-destructive evaluation of structures made by additive manufacturing, and detection of hard-to-inspect locations in unitised structures.Read moreRead less
A novel physical-digital approach for the assessing a large critical asset. This project aims to deliver an artificial intelligence-enabled decision-making tool to maintain and manage the floating covers of vast lagoons that treat raw sewage. The cover harvests the biogas released from the anaerobic digestion of sewage for electric power generation that exceeds the plant’s requirement. The approach involves an innovative thermographic technique and exploits transfer learning to adapt neural netw ....A novel physical-digital approach for the assessing a large critical asset. This project aims to deliver an artificial intelligence-enabled decision-making tool to maintain and manage the floating covers of vast lagoons that treat raw sewage. The cover harvests the biogas released from the anaerobic digestion of sewage for electric power generation that exceeds the plant’s requirement. The approach involves an innovative thermographic technique and exploits transfer learning to adapt neural networks trained on lab-scale and synthetic data to field implementation. The outcome is a machine learning framework to optimise biogas harvesting and renewable energy generation, and to avoid structural failure, that is capable of continuous improvement to take into account improved data and/or modelling capabilities.Read moreRead less