Limited-Data-Driven Machine Learning in Structural Health Diagnosis
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49791-9_2
Reference18 articles.
1. Farrar, C., Lieven, N.: Damage prognosis: the future of structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365(1851), 623-632 (2007).
2. Bao, Y., Li, H.: Machine learning paradigm for structural health monitoring. Structural Health Monitoring 20(4), 1353-1372 (2021).
3. Bao, Y., Chen, Z., Wei, S., Tang, Z., Xu, Y., Li H.: The state of the art of data science and engineering in structural health monitoring. Engineering 5(2), 234-242 (2019).
4. Xu, Y., Qian, W., Li, N., Li, H.: Typical advances of artificial intelligence in civil engineering. Advances in Structural Engineering 25(16): 3405-3424 (2022).
5. Qiao, W., Zhao, Y., Xu, Y., Lei, Y., Wang, Y., Yu, S., Li H.: Deep learning-based pixel-level rock fragment recognition during tunnel excavation using instance segmentation model. Tunnelling and Underground Space Technology 115, 104072 (2021).
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