CaltechAUTHORS
  A Caltech Library Service

Statistical system identification and applications to seismic response of structures

Yang, Chi-Ming (1996) Statistical system identification and applications to seismic response of structures. California Institute of Technology . (Unpublished) http://resolver.caltech.edu/CaltechEERL:1996.EERL-96-02

[img]
Preview
PDF (Adobe PDF (8 MB))
See Usage Policy.

7Mb

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechEERL:1996.EERL-96-02

Abstract

A pragmatic and versatile statistical system identification framework is presented and applied to seismic response records of structures. The framework is based on the interpretation of probability as a measure of plausibility and on Bayesian statistical inference. Various classical system identification techniques can be derived and viewed as the special cases of the framework. However, the framework can provide a more informative interpretation of the identified optimal model. When the number of sampled input and output data from structures is large, useful asymptotic approximations of the analytical results are available. These asymptotic approximations are incorporated into the framework by introducing the definitions of system identifiability and model identifiability. New asymptotic approximation results are derived for the system un-identifiable case. From the viewpoint of asymptotic approximations, the system identification problem is a non-trivial global optimization problem. Two generalized trajectory methods, the homotopy scheme and the relaxation scheme, are presented which can be combined to provide a very robust numerical procedure for global optimization. Both methods can also be applied to find the roots of a set of nonlinear algebraic equations. Structural model updating is useful because it can be applied to structural health monitoring and is also desirable since the theoretically based stiffness matrix of a structure can be improved by using the measured structural response data. However, no well-accepted solution to this difficult problem has emerged primarily because it is an ill-conditioned and non-unique inverse problem. A single-stage structural model updating approach using the least-squares prediction-error system identification method and a substructuring technique is proposed and applied to simulated and real structural response data.


Item Type:Report or Paper (Technical Report)
Group:Earthquake Engineering Research Laboratory
Record Number:CaltechEERL:1996.EERL-96-02
Persistent URL:http://resolver.caltech.edu/CaltechEERL:1996.EERL-96-02
Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:26328
Collection:CaltechEERL
Deposited By: Imported from CaltechEERL
Deposited On:05 Sep 2001
Last Modified:26 Dec 2012 13:56

Repository Staff Only: item control page