Andrew W. Smyth
Andrew Smyth specializes in structural health monitoring, using sensor information to determine the condition of critical infrastructure. Recently his interest in sensor network monitoring has expanded to large fleets of vehicles in urban environments. Smyth is the chair of the Smart Cities Center at Columbia University’s Data Science Institute.
Smyth has been involved with the sensor instrumentation and vibration analysis and remote monitoring of a large number of iconic long-span bridges and landmark buildings and museums. His research interests include the development of data fusion and system identification algorithms to derive maximum information from large heterogeneous sensor networks monitoring dynamical systems, nonlinear system dynamical modeling and simulation, and natural hazards risk assessment.
Prof. Smyth received his Sc.B. and A.B. degrees at Brown University in 1992 in Civil Engineering and Architectural Studies respectively. He received his M.S. in Civil Engineering at Rice in 1994, an M.S. in Electrical Engineering (1997) and his Ph.D. in Civil Engineering (1998) at the University of Southern California.
- Visiting Researcher at KU Leuven, Belgium, Spring 2014
- Visiting Researcher at Laboratoire Central des Ponts et Chausées, Paris, Jan.-Aug., 2007
- Research Associate, Univ. of Southern California, May - June 1998
- Professor, Columbia University, July 2010 to present
- Chair, Smart Cities Center of the Data Science Institute, Columbia University, 2013 - present
- Director of Research, Robert A.W. Carleton Laboratory, Columbia University, 2013 - present
- Associate Professor, Columbia University, July 2003 to June 2010
- Assistant Professor, Columbia University, July 1998 to June 2003
- American Society of Civil Engineers
- American Society of Mechanical Engineers
- International Association of Structural Control and Monitoring
- Fellow, ASCE Engineering Mechanics Institute 2013
- ASCE Walter L. Huber Civil Engineering Research Prize, 2008
- National Science Foundation CAREER award, 2002
- Jang, J., Yang, Y., Smyth, A.W., Cavalcanti, D., Kumar, Framework of Data Acquisition and integration for the detection of pavement distress via multiple vehicles, R., Jo. of Computing in Civil Engineering, vol. 31, no. 2, 2017
- Jang, J., and Smyth, A.W., Bayesian model updating of a full-scale finite element model with a sensitivity-based clustering, Jo. of Structural Control and Health Monitoring, 2017
- Olivier, A., and Smyth, A.W.,Particle filtering and marginalization for parameter identification in structural systems, Jo. of Structural Control and Health Monitoring, vol. 24, no. 3, 2017
- Brewick, P.T., Smyth, A.W., Combining optimization methods with response spectra curve-fitting toward improved damping ratio estimation, Structural Control and Health Monitoring, article in press, 2017.
- Smyth, A.W., Brewick, P., Greenbaum, R., Chatzis, M., Serotta, A., Stunkel, I., Vibration Mitigation and Monitoring: A Case Study of Construction in a Museum, Journal of the American Institute for Conservation, 55 (1), pp. 32-55, 2016.
- Kontoroupi, T., Smyth, A.W., Online Noise Identification for Joint State and Parameter Estimation of Nonlinear Systems, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, 2015/9/24, doi: 10.1061/AJRUA6.0000839.
- Greenbaum, R.J., Smyth, A.W., Chatzis, M.N., A Monocular Computer Vision Method for the Experimental Study of Three Dimensional Rocking Motion, ASCE Jo. of Engineering Mechanics, vol. 142, no. 1, . DOI: 10.1061/(ASCE)EM.1943-7889.0000972, 2015
- Chatzis, M.N., and Smyth, A.W., Modeling of the 3D Rocking Problem, International Journal of Non-Linear Mechanics 47 (4) , pp. 85-98, 2012
- Chatzi, E. and Smyth A.W., The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing, Structural Control and Health Monitoring, Volume 16, Issue 1, Date: February 2009, Pages: 99-123.
- Smyth, A.W. and Wu, M., Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring, Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 706-723, Feb. 2007.