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Materials Science and Nanotechnology | Volume: 03
WORLD CONGRESS ON SMART MATERIALS AND STRUCTURES
&
3
rd
International Conference on
POLYMER CHEMISTRY AND MATERIALS ENGINEERING
November 21-22, 2019 | Singapore
Joint event on
S
ystem identification methods may be used to update a
parameterized model of a structure based on features
extracted from its measured data such as structural
vibration responses or guided-wave/ultrasonic NDT
signals. A common goal in applying system identification
to structural health monitoring is to infer damage/flaw
by updating structural model parameters. However, no
structural model is an exact representation of a structure’s
behavior, because parameter estimation often gives non-
unique results, raising the issue of model identifiability. A
Bayesian framework has been developed that addresses
this difficulty. The relative plausibility of all plausible values
of the parameters based on the data is quantified by the
posterior PDF (probability density function) coming from
Bayes’ Theorem. Another powerful feature of the Bayesian
framework is that it implements an elegant and powerful
version of Ockham’s Razor, known as the Bayesian Ockham
Razor. It trades off the fit to the data by the model against
the amount of information extracted from the data and can
automatically avoid over-fitting of the sensor data. Sparse
Bayesian learning is a supervised learning framework
that is very effective at implementing Bayesian Ockham
Razor by achieving sparse representations in the context
of regression and classification. We will give an overview
of our recent progress of developing sparse Bayesian
learning algorithms for performing sparse stiffness loss
inference for vibration-based damage assessment and
also for flaw detection using guided-wave/ultrasonic NDT
signal processing. It will be shown that the incorporation
of prior knowledge pertaining to the spatial sparseness
of structural damage/flaw helps to suppress the possible
occurrences of false and missed damage/flaw detections.
Several nice features of our theory from both theoretical
and computational perspectives will also be discussed.
This research is a joint work with Prof. James L. Beck at the
California Institute of Technology and Prof. Hui Li at the
Harbin Institute of Technology.
Biography
Yong Huang has completed his Ph.D. in Engineering Mechanics at the age
of 28 from Harbin Institute of Technology, China. He is the full professor
of Civil Engineering at the Harbin Institute of Technology, China. He was a
Postdoctoral Scholar and Visiting Associate in department of Mechanical
and Civil Engineering at the California Institute of Technology during
the periods of February 2012 until February 2013 and December 2014
until February 2017, respectively. He has over 50 technical publications
that have been cited over 400 times, covering topics in structural health
monitoring, signal processing, system identification, guided-wave
testing, ultrasonic NDT, machine learning. In much of this research he
uses a Bayesian probabilistic treatment of modeling uncertainty that is
based on probability as a multi-valued conditional logic for quantitative
plausible reasoning. He has been serving as an editorial board member of
International Journal on Data Science and Technology.
e:
huangyong@hit.edu.cnYong Huang
Harbin Institute of Technology, China
Sparse Bayesian learning for system identification and damage
assessment in structural health monitoring
Mater Sci Nanotechnol, Volume: 03