Civil and Environmental Engineering Department Seminar: Nonlinear Bayesian filtering for damage assessment and monitoring of structures
Seminar | February 24 | 12-1 p.m. | 542 Davis Hall
Recent years have seen a concurrent development of new sensor technologies and high‐fidelity modeling capabilities. At the junction of these two topics lies an opportunity for real‐time system monitoring and damage assessment of structures. In this context, on‐line Bayesian parameter identification and filtering methods, which rely on measurements to monitor the dynamic states and parameters representing a system, provide a very accurate and efficient tool for damage detection and characterization.
Such Bayesian inference methods are very attractive for damage identification and characterization due to their ability to take into account uncertainties in the system and measurements, as well as stochastic input excitations, and yield results in a probabilistic format thus enabling more accurate damage assessment of civil structures. These methods can also handle ill‐conditioned problems, where not all parameters can be learnt from the available noisy data, a problem which will surely arise when considering large dimensional complex systems.
A major challenge regarding on‐line Bayesian filtering algorithms lies in achieving good accuracy for large
dimensional nonlinear, potentially non‐Gaussian, systems. Using algorithmic enhancements of filtering techniques, mainly based on innovative ways to reduce the dimensionality of the problem at hand, one can obtain a good trade‐off between accuracy and computational complexity of the learning algorithms, a key point crucial to enabling fast decision‐making procedures.
These methods can potentially find a wider variety of applications; Bayesian model class selection offers
possibilities for improved modeling of structures based on available measurements, state filtering offers capabilities in monitoring of pollution emissions, traffic and beyond.