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Nikolaos Dervilis

The following bibliography contains all publications indexed in this database that are linked with this name as either author, editor or any other kind of contributor.

  1. Fremmelev, Mads Anker / Ladpli, Purim / Orlowitz, Esben / Dervilis, Nikolaos / McGugan, Malcolm / Branner, Kim: A full-scale wind turbine blade monitoring campaign: detection of damage initiation and progression using medium-frequency active vibrations. In: Structural Health Monitoring.


  2. Poole, Jack / Gardner, Paul / Dervilis, Nikolaos / Bull, Lawrence / Worden, Keith (2022): On statistic alignment for domain adaptation in structural health monitoring. In: Structural Health Monitoring, v. 22, n. 3 (September 2022).


  3. Gardner, Paul / Bull, Lawrence A. / Dervilis, Nikolaos / Worden, Keith (2022): Domain-adapted Gaussian mixture models for population-based structural health monitoring. In: Journal of Civil Structural Health Monitoring, v. 12, n. 6 (August 2022).


  4. Simpson, Thomas / Dervilis, Nikolaos / Chatzi, Eleni (2021): Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks. In: Journal of Engineering Mechanics (ASCE), v. 147, n. 10 (October 2021).


  5. Bull, Lawrence A. / Gardner, Paul / Rogers, Timothy J. / Cross, Elizabeth J. / Dervilis, Nikolaos / Worden, Keith (2021): Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data. In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, v. 7, n. 1 (March 2021).


  6. Worden, Keith / Bull, Lawrence A. / Gardner, Paul / Gosliga, Julian / Rogers, Timothy J. / Cross, Elizabeth J. / Papatheou, Evangelos / Lin, Weijiang / Dervilis, Nikolaos (2020): A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring. In: Frontiers in Built Environment, v. 6 (January 2020).


  7. Abdessalem, Anis Ben / Dervilis, Nikolaos / Wagg, David J. / Worden, Keith (2018): Automatic Kernel Selection for Gaussian Processes Regression with Approximate Bayesian Computation and Sequential Monte Carlo. In: Frontiers in Built Environment, v. 3 (February 2018).


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