Generative Adversarial Networks for Structural Damage Diagnostics

  • Method and system for monitoring the health of civil structures and predicting damage
  • Deep-learning-based generative model can provide dynamic responses for conditions affecting a structure throughout its life cycle

Abstrato

The University of Central Florida invention describes deep-learning-based generative model methods—Generative Adversarial Networks (GAN)—for civil structural health monitoring and damage prediction. Damage diagnostic data is limited, as data collection can be costly and challenging or because there are not enough data from damaged areas to train detection models. Using information collected from multiple structures, synthetic data samples can be generated by state-of-the-art GAN models and used to train damage diagnostic systems. These tools could aid engineers, stakeholders, and decision-makers (1) to perform diagnostics accurately and effectively on raw acceleration signals and (2) to be proactive rather than reactive in managing the life cycle of structures.

Partnering Opportunity

The research team is seeking partners for licensing and/or research collaboration.

Stage of Development

Prototype available.

Local na rede Internet

https://ucf.flintbox.com/technologies/C89DDD1BA3304F158D845D2C91C8086E

Vantagens

  • Minimizes the need for dynamic response data collected from structures by generating data samples to fulfill the need of any class
  • Increases the performance of the AI models used for vibration-based damage diagnostics
  • Experiments yield 97 percent classification accuracy for synthetically enhanced datasets

Aplicações potenciais

  • Civil structural condition assessment services

Informações de contato

Nome: Raju Nagaiah

E-mail: raju@ucf.edu

Telefone: 407.882.0593