Fatigue Analysis on Four Months of Data on a Steel Railway Bridge: Event Detection and Train Features’ Effect on Fatigue Damage
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-07322-9_67
Reference11 articles.
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3. Arnold, M., Keller, S.: Detection and classification of bridge crossing events with ground-based interferometric radar data and machine learning approaches. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 5(1), 109-116. (2020). https://doi.org/10.5194/isprs-annals-V-1-2020-109-2020
4. Arnold, M., Hoyer, M., Keller, S.: Convolutional neural networks for detecting bridge crossing events with ground-based interferometric radar data. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. V-1–2021, 31–38 (2021). https://doi.org/10.5194/isprs-annals-V-1-2021-31-2021
5. Frøseth, G.T., Rönnquist, A.: Finding the train composition causing greatest fatigue damage in railway bridges by Late Acceptance Hill Climbing. Eng. Struct. 196, 109342 (2019). https://doi.org/10.1016/j.engstruct.2019.109342
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