Dr. Danny Smyl, Assistant Professor
Education
- Ph.D., Civil Engineering, North Carolina State University
- M.S., Civil Engineering, University of Kansas
- B.S., Civil Engineering, University of Kansas
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Postgraduate Teaching Certificate, University of Sheffield
Research
Dr. Smyl's research principally focuses on developing tools to better understand, monitor, and characterize life cycle processes of structures and materials. To do this, he integrates tomographic, machine learning, and inversion based numerical approaches to delve deeper into data and unlock key processes that cannot be unveiled using conventional methodologies. The long-term end state of his research is to improve the safety, resilience, longevity, and environmental friendliness of civil infrastructure by integrating intelligent algorithms and meaningful spatial-temporal data acquisition. In addition to research in civil and structural engineering, he is actively involved in research within inverse problems, applied mathematics, and medical imaging.
Publications
Selected Recent Publications (2020 -)
Liang Chen, Adrien Gallet, Shan-Shan Huang, Dong Liu, and Danny Smyl. “Probabilistic Cracking Prediction via Deep Learned Electrical Tomography.” Structural Health Monitoring, (2021).
Danny Smyl, Tyler N. Tallman, Jonathan A. Black, Andreas Hauptmann, and Dong Liu. "Learning and correcting non-Gaussian model errors." Journal of Computational Physics (2021).
Andreas Hauptmann and Danny Smyl. "Fusing electrical and elasticity imaging." Philosophical Transactions of the Royal Society A (2021).
Danny Smyl, Tyler N. Tallman, Dong Liu, and Andreas Hauptmann. "An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values." IEEE Signal Processing Letters (2021).
Adam A. Dennis, Jordan J. Pannell, Danny Smyl, and Samuel E. Rigby. "Prediction of blast loading in an internal environment using artificial neural networks." International Journal of Protective Structures (2021).
Tyler N. Tallman and Danny Smyl. "Structural health and condition monitoring via electrical impedance tomography in self-sensing materials: a review." Smart Materials and Structures (2020).
Danny Smyl, Sven Bossuyt, Waqas Ahmad, Anton Vavilov, and Dong Liu. "An overview of 38 least squares–based frameworks for structural damage tomography." Structural Health Monitoring (2020).
Danny Smyl and Dong Liu. "Optimizing Electrode Positions in 2-D Electrical Impedance Tomography Using Deep Learning." IEEE Transactions on Instrumentation and Measurement (2020).
Danny Smyl and Dong Liu. "Less is often more: Applied inverse problems using hp-forward models." Journal of Computational Physics (2020).
Danny Smyl. "Electrical tomography for characterizing transport properties in cement-based materials: A review." Construction and Building Materials (2020).