When is an SHM problem a Multi-Task-Learning problem?

Sarah Bee, Lawrence Bull, Nikolas Dervilis, Keith Worden
5/16/2023
cs.LG

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Abstract

Multi-task neural networks learn tasks simultaneously to improve individual task performance. There are three mechanisms of multi-task learning (MTL) which are explored here for the context of structural health monitoring (SHM): (i) the natural occurrence of multiple tasks; (ii) using outputs as inputs (both linked to the recent research in population-based SHM (PBSHM)); and, (iii) additional loss functions to provide different insights. Each of these problem settings for MTL is detailed and an example is given.

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Cite this paper

@article{bee2023when,
  title  = {When is an SHM problem a Multi-Task-Learning problem?},
  author = {Sarah Bee and Lawrence Bull and Nikolas Dervilis and Keith Worden},
  year   = {2023},
  eprint = {2305.09425},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.09425v1}
}

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