ALIAS · Machine Learning for Structural Integrity Assessments
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-07-01 → 2023-06-30
- EU contribution
- €184,591
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Machine Learning for Structural Integrity Assessments
The continued safe operation of critical infrastructure is key in ensuring economic prosperity. The drive towards carbon neutrality presents significant challenges to engineers as increasing operational efficiency often results in harsh, unfavourable operating conditions. The cost of implementing more efficient processes is a reduction in material performance resulting in prohibitively short component lifetimes. Material damage is multifactorial and depends strongly on the material chemistry, loading conditions, and operating environment. Recent advances in machine learning offer a potential solution to uncovering the complex interactions that inform the material damage process. By bridging the knowledge gaps currently impeding SIA improvement engineers can safely operate key infrastructure more efficiently and continue operating for longer periods of time.
Data: CORDIS, © European Union
Project objective
The continued safe operation of critical infrastructure is key in ensuring economic prosperity. The drive towards carbon neutrality presents significant challenges to engineers as increasing operational efficiency often results in harsh, unfavourable operating conditions (e.g. offshore wind turbines). The cost of implementing more efficient processes is a reduction in materials performance resulting in prohibitively short component lifetimes. The challenge facing engineers lies in improving the structural integrity assessment (SIA) methods. Current SIA methods are predominately stress-based and thus, inherently dominated by the yield strength of the component material. Non-linear materials such as steels typically fail by strain induced plasticity where significant additional energies are adsorbed prior to fracture. Strain-based assessments contain considerable built in conservatisms that have not yet been explored. The principal aims of this fellowship application are to develop more advanced SIA methods by considering conservatisms in existing stress-based and strain-based approaches and to exploit recent advances in machine learning to identify and predict key parameters influencing transformative damage in fracture toughness testing. The fundamental understanding of material damage generated in this work will reduce knowledge gaps currently impeding SIA improvement. The benefits of this work include advances to multiple international codes and standards, the continued safe operation of aging critical infrastructure, longer more realistic estimated lifetimes for new components and, significant industrial cost savings through enhanced component design, reduced maintenance cost and reduction in early structure retirement.
Original text from CORDIS.
Participants
- UNIVERSITY OF LIMERICK · LimerickCoordinatorIreland
Links
Data: CORDIS, © European Union