Unscented Kalman filter with unknown input and weighted global iteration for health assessment of large structural systems

2015 ◽  
Vol 23 (1) ◽  
pp. 156-175 ◽  
Author(s):  
Abdullah Al-Hussein ◽  
Achintya Haldar
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Health Assessment ◽  
Structural Systems ◽  
Unknown Input ◽  
Global Iteration

Novel Unscented Kalman Filter for Health Assessment of Structural Systems with Unknown Input

2015 ◽  
Vol 141 (7) ◽  
pp. 04015012 ◽  
Author(s):  
Abdullah Al-Hussein ◽  
Achintya Haldar
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Health Assessment ◽  
Structural Systems ◽  
Unknown Input

scholarly journals Complexities in Assessing Structural Health of Civil Infrastructures

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Abdullah Al-Hussein ◽  
Achintya Haldar
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Health Assessment ◽  
Time History ◽  
Dynamic Responses ◽  
Simple Problem ◽  
Civil Infrastructures ◽  
Duration Time ◽  
Highly Nonlinear ◽  
Global Iteration

The complexity in the health assessment of civil infrastructures, as it evolves over a long period of time, is briefly discussed. A simple problem can become very complex based on the current needs, sophistication required, and the technological advancements. To meet the current needs of locating defect spots and their severity accurately and efficiently, infrastructures are represented by finite elements. To increase the implementation potential, the stiffness parameters of all the elements are identified and tracked using only few noise-contaminated dynamic responses measured at small part of the infrastructure. To extract the required information, Kalman filter concept is integrated with other numerical schemes. An unscented Kalman filter (UKF) concept is developed for highly nonlinear dynamic systems. It is denoted as 3D UKF-UI-WGI. The basic UKF concept is improved in several ways. Instead of using one long duration time-history in one global iteration, very short duration time-histories and multiple global iterations with weight factors are used to locate the defect spot more accurately and efficiently. The capabilities of the procedure are demonstrated with the help of two informative examples. The proposed procedure is much superior to the extended Kalman filter-based procedures developed by the team earlier.

A Two-stage Parametric Identification of Strong Nonlinear Structural Systems with Incomplete Response Measurements

2016 ◽  
Vol 16 (04) ◽  
pp. 1640022 ◽  
Author(s):  
Lijun Liu ◽  
Ying Lei ◽  
Mingyu He
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Structural Parameters ◽  
Nonlinear Problems ◽  
Parametric Identification ◽  
Structural Systems ◽  
Two Stage ◽  
Incomplete Response ◽  
Nonlinear Structural ◽  
Structural Responses

Compared with the identification of linear structural parameters, it is more difficult to conduct parametric identification of strong nonlinear structural systems, especially when only incomplete structural responses are available. Although the extended Kalman filter (EKF) is useful for structural identification with partial measurements of structural responses and can be extended for the identification of nonlinear structures, EKF approximates nonlinear system through Taylor series expansion and is therefore not effective for the identification of strong nonlinear structural systems. Other approaches such as the unscented Kalman filter (UKF) have been proposed for the identification of strong nonlinear problems. Based on the fact that nonlinearities exist in local areas of structures, a straightforward two-stage identification approach is proposed in this paper for the identification of strong nonlinear structural parameters with incomplete response measurements. In the first stage, the locations of nonlinearities are identified based on the EKF for the identification of the equivalent linear structures. In the second stage, the UKF is utilized to identify the parameters of strong nonlinear structural systems. Therefore, the parametric identification of strong nonlinear structural parameters is simplified by the proposed approach. Several numerical examples with different nonlinear models and locations are used to validate the proposed approach.

Hybrid simulation of structural systems with online updating of concrete constitutive law parameters by unscented Kalman filter

2017 ◽  
Vol 25 (2) ◽  
pp. e2069 ◽  
Author(s):  
Zhu Mei ◽  
Bin Wu ◽  
Oreste S. Bursi ◽  
Ge Yang ◽  
Zhaoran Wang
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Hybrid Simulation ◽  
Constitutive Law ◽  
Structural Systems

Three-stage unscented Kalman filter for state and fault estimation of nonlinear system with unknown input

2017 ◽  
Vol 354 (18) ◽  
pp. 8421-8443 ◽  
Author(s):  
Mengli Xiao ◽  
Yongbo Zhang ◽  
Huimin Fu
Keyword(s):  
Kalman Filter ◽  
Nonlinear System ◽  
Unscented Kalman Filter ◽  
Fault Estimation ◽  
Unknown Input
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