SOURCE-LINKED INTELLIGENCE

Digital Twin-Empowered Intelligent Maintenance of Buried Infrastructure through Investigating Their Deterioration and Resilience Mechanisms

CORDIS · observation · Publication date unknown

ith their physical BI twin on a regular basis, adversely affecting their ability to automatically advise the BI operator about maintenance schedules. DigitalBI will: 1) Develop new adaptive denoising deep learning (DL) methods and image-based numerical simulation methods to unravel deterioration mechanisms; 2) Develop a new numerical coupling method and physics-encoded neural networks to decipher the resilience mechanisms; and 3) develop a large multimodal model (TunnelGPT) to integrate approaches developed for the investigation of deterioration and resilience mechanisms to form a digital twin model for optimizing maintenance measures. This will combine the researcher's experience in deep learning-based BI maintenance with the supervisors' expertise in physics-informed DL and large language models. DigitalBI will be expected to set the researcher for a career in academia in Europe, while improving the comprehension of BI's deterioration and resilience mechanisms to reduce maintenance costs and consumption of materials for the remediation of BI. DigitalBI will also enable policymakers

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recordType

award

status

SIGNED

region

EU

value

276187.92

unit

EUR