Titlebook: Data Science in Engineering, Volume 9; Proceedings of the 3 Ramin Madarshahian,Francois Hemez Conference proceedings 2022 The Society for E

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https://doi.org/10.1007/978-981-19-2519-1rce Research Lab’s DROPBEAR apparatus, exhibiting accuracy on par with MLPs trained offline. Results show that these two algorithms serve as viable candidates for real-time structural health monitoring applications.

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Optimization Algorithms Surpassing Metaphortor-level mistuning identification technique using a feed-forward neural network is presented. Using this approach, mistuning prediction for individual sectors is achieved using only a subset of forced responses from within a sector. The knowledge or use of system modal response information is not r

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Laura-Nicoleta Ivanciu,Gabriel Olteanng that the structure’s response is represented by points in a manifold, part of the space will be formed due to variations in external conditions affecting the structure. This idea proves efficient in SHM, as it is exploited to generate structural data for specific values of environmental coefficie

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https://doi.org/10.1007/978-1-4614-7245-2tional data-based methods on the post-repair data. Transfer learning, in the form of domain adaptation, provides a solution to this problem, allowing knowledge from the pre-repair labels to be transferred to the post-repair dataset by forming a shared latent space where the pre- and post-repair data

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https://doi.org/10.1007/978-3-540-70778-3the population, creating a single classification model that generalises across the complete population. This paper explores ., a branch of transfer learning where datasets have inconsistent feature spaces, i.e. the dimensions of datasets from one structure are different to those from another. In PBS