Active-Sensing Structural Health Monitoring via Statistical Learning: An Experimental Study Under Varying Damage and Loading States
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-64908-1_43
Reference20 articles.
1. Ahmed, S., Amer, A., Varela, C., Kopsaftopoulos, F.P.: Data driven state awareness for fly-by-feel aerial vehicles via adaptive time series and Gaussian process regression models. In: Proceedings of the InfoSymbiotics/DDDAS2020 Conference. Springer (2020)
2. Ahmed, S., Kopsaftopoulos, F.P.: Investigation of broadband high-frequency stochastic actuation for active-sensing SHM under varying temperature. In: Proceedings of the 12th International Workshop on Structural Health Monitoring, IWSHM 2019, Palo Alto, CA, USA (September 2019)
3. Amer, A., Kopsaftopoulos, F.P.: Probabilistic damage quantification via the integration of non-parametric time-series and Gaussian process regression models. In: Proceedings of the 12th International Workshop on Structural Health Monitoring, IWSHM 2019, Palo Alto, CA, USA, pp. 2384–2393 (September 2019)
4. Amer, A., Kopsaftopoulos, F.P.: Towards unified probabilistic rotorcraft damage detection and quantification via non-parametric time series and Gaussian process regression models. In: Proceedings of the Vertical Flight Society 76th Annual Forum & Technology Display, Virginia Beach, VA, USA (October 2020)
5. Avendaño-Valencia, L.D., Chatzi, E.N., Koo, K.Y., Brownjohn, J.M.: Gaussian process time-series models for structures under operational variability. Front. Built Environ. 3, 69 (2017)
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