Danny Smyl

Danny Smyl
Structural Engineering, Mechanics and Materials
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Danny Smyl

Assistant Professor
Email Address
danny.smyl@ce.gatech.edu
Office Building
CODA
Office Room Number
1642
Biography

Dr. Danny Smyl is an Assistant Professor in the School of Civil and Environmental Engineering at the Georgia Institute of Technology. His research develops tools to design, monitor, and characterize the life-cycle behavior of structures and materials by integrating tomographic sensing, inverse problems, and machine learning with uncertainty quantification. Danny is an Associate Editor for the Journal of Nondestructive Evaluation and active in ACI where he serves as a voting member in committees 123 and 135. He was previously an officer in the U.S. Marine Corps as a Combat Engineer officer including a tour in Afghanistan.  

Research

Dr. Smyl’s group works at the interface of inverse problems, machine learning, and sensing to enable reliable model-informed decisions in structural engineering. Current themes include: (i) tomography and data-driven reconstruction for damage/condition imaging in self-sensing materials; (ii) learned model-error compensation and uncertainty quantification for forward and inverse models; and (iii) machine learning-driven structural design and optimization workflows that integrate physics constraints and code-relevant performance metrics.  

Education
  1. Ph.D., Civil Engineering                        North Carolina State University                      2017 
  2. M.S., Civil Engineering                                          University of Kansas                        2012 
  3. B.S., Civil Engineering                                          University of Kansas                         2011 
  4. Postgraduate Teaching Certificate                     University of Sheffield                      2021 
Teaching

Dr. Smyl teaches and develops courses that connect core mechanics and structural engineering with modern computational methods. Teaching interests include mechanics of deformable bodies, civil engineering materials, structural analysis and design, inverse problems and signal processing for engineers, probabilistic/decision methods, and practical machine learning and scientific programming for civil and environmental engineering. 

Distinctions & Awards
  • AFRL Research Fellow (2024) 
  • EPSRC Engineering Early Career Forum Member (2019-2021) 
  • Fulbright Grant recipient, Finland (2016-2017) 
  • Postgraduate Teaching Certificate, University of Sheffield (2021) 
  • SuperVisionary / PhD Supervisor Award (Engineering Faculty, Sheffield)  
  • Paul Zia Fellowship Award (NCSU) 
Publications

  1. Smyl, D., Zhuang, B., Rigby, S., Bruun, E. P. G., Jones, B., Kastner, P., Tien, I., & Gallet, A. (2025). OpenPyStruct: Open-source toolkit for machine learning-driven structural optimization. Engineering Structures, 343, 120869. doi:10.1016/j.engstruct.2025.120869 

  1. Smyl, D., Tallman, T. N., Homa, L., Flournoy, C., Hamilton, S. J., & Wertz, J. (2025). Physics informed neural networks for electrical impedance tomography. Neural Networks, 188, 107410. doi:10.1016/j.neunet.2025.107410 

  1. Gallet, A., Hajirasouliha, I., Liew, A., & Smyl, D. (2024). Influence zones of continuous beam systems. Structures, 68(1), 107069. doi:10.1016/j.istruc.2024.107069 

  1. Gallet, A., Liew, A., Hajirasouliha, I., & Smyl, D. (2024). Machine learning for structural design models of continuous beam systems via influence zones. Inverse Problems, 40(5), 055011. doi:10.1088/1361-6420/ad3334 

  1. Gallet, A., Rigby, S., Tallman, T.N., Kong, X., Hajirasouliha, I., Liew, A., Liu, D., Chen, L., Hauptmann, A. & Smyl, D., (2022). Structural engineering from an inverse problems perspective. Proceedings of the Royal Society A, 478(2257), p.20210526. 

  1. Chen, L., Gallet, A., Huang, S. S., Liu, D., & Smyl, D. (2022). Probabilistic cracking prediction via deep learned electrical tomography. Structural Health Monitoring, 21(4), 1574–1589. doi:10.1177/14759217211037236 

  1. Kong, X. and Smyl, D., (2022), September. Investigation of the condominium building collapse in Surfside, Florida: A video feature tracking approach. In Structures (Vol. 43, pp. 533-545). Elsevier. 

  1. Smyl, D., Tallman, T. N., Black, J. A., Hauptmann, A., & Liu, D. (2021). Learning and correcting non-Gaussian model errors. Journal of Computational Physics, 432, 110152. doi:10.1016/j.jcp.2021.110152 

  1. Tallman, T.N. and Smyl, D., (2020). Structural health and condition monitoring via electrical impedance tomography in self-sensing materials: a review. Smart Materials and Structures, 29(12), p.123001. 

  1. Smyl, D. and Liu, D., (2020). Less is often more: Applied inverse problems using hp-forward models. Journal of Computational Physics, 399, p.108949.