Abstract
Researchers detail new data in artificial intelligence. According to news reporting from Sheffield, United Kingdom, by NewsRx journalists, research stated, "A significant challenge of structural health monitoring (SHM) is the lack of labeled data collected from damage states." Funders for this research include Deutsche Forschungsgemeinschaft. The news reporters obtained a quote from the research from Leibniz University: "Consequently, the collected data can be incomplete, making it difficult to undertake machine learning tasks, to detect or predict the full range of damage states a structure may experience. Transfer learning is a helpful solution, where data from (source) structures containing damage labels can be used to transfer knowledge to (target) structures, for which damage labels do not exist. Machine learning models are then developed that generalize to the target structure. In practical applications, it is unlikely that the source and the target structures contain the same damage states or experience the same environmental and operational conditions, which can significantly impact the collected data. This is the first study to explore the possibility of transfer learning for damage localisation in SHM when the damage states and the environmental variations in the source and target datasets are disparate. Specifically, using several domain adaptation methods, this article localizes severe damage states at a target structure, using labeled information from minor damage states at a source structure."
NETL
NSTL