A Bayesian probabilistic approach is presented for selecting the most plausible class of models for a structural or mechanical
system within some specified set of model classes, based on system response data. The crux of the approach is to rank the classes of
models based on their probabilities conditional on the response data which can be calculated based on Bayes’ theorem and an asymptotic
expansion for the evidence for each model class. The approach provides a quantitative expression of a principle of model parsimony or
of Ockham’s razor which in this context can be stated as "simpler models are to be preferred over unnecessarily complicated ones."
Examples are presented to illustrate the method using a single-degree-of-freedom bilinear hysteretic system, a linear two-story frame, and
a ten-story shear building, all of which are subjected to seismic excitation.

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Model Selection using Response Measurements: Bayesian Probabilistic Approach

Introduction -- What are Smart Structures and Materials? Smart Structures -- The Instrumented Materials. Structural and Mesoscopic Materials. Molecular Materials -- An Engineering View. Nuclear Materials. Applications Examples. Scenario Building and the Future.

Smart Structures and Materials

The identification of structural damage is an important objective of health monitoring for civil infrastructures. System identification and damage detection based on measured vibration data have received intensive studies recently. Frequently, damage to a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, we propose an adaptive tracking technique, based on the extended Kalman filter approach, to identify the structural parameters and their changes when vibration data involve damage events. The proposed technique is capable of tracking the changes of system parameters from which the event and severity of structural damage may be detected on-line. Our adaptive filtering technique is based on the current measured data to determine the parametric variation so that the residual error of the estimated parameters is contributed only by noise. This technique is applicable to linear and nonlinear structures. Simulation results for tracking the parametric changes of nonlinear elastic, hysteretic and linear benchmark structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting structural damage, using measured vibration data. Copyright © 2005 John Wiley & Sons, Ltd.

An adaptive extended Kalman filter for structural damage identification

Over the past few decades, structural system identification based on vibration measurements has attracted much attention in the structural dynamics field. The well-known extended Kalman filter (EKF) is often used to deal with nonlinear system identification in many civil engineering applications. In spite of that, applying an EKF to highly nonlinear structural systems is not a trivial task, particularly those subject to severe loading. Unlike the EKF, a new technique, the unscented Kalman filter (UKF) is applicable to highly nonlinear systems. In this paper, the EKF and UKF are compared and applied for nonlinear structural system identification. Simulation results show that the UKF produces better state estimation and parameter identification than the EKF and is also more robust to measurement noise levels. Copyright © 2006 John Wiley & Sons, Ltd.

Application of the unscented Kalman filter for real-time nonlinear structural system identification

A review on prognostic techniques for non-stationary and non-linear rotating systems

The extended Kalman filter is applied to system identification problems of seismic structural systems. In order to obtain the stable and convergent solutions, a weighted global iteration procedure with an objective function is proposed for stable estimation, being incorporated into the extended Kalman filter algorithm. For the effectiveness of this present proposal, the identification problems are investigated for multiple degree-of-freedom linear systems, bilinear hysteretic systems, and equivalent linearization of bilinear hysteretic systems. As numerically shown examples, the weighted global iteration procedure may be useful to identification problems.

Structural Identification by Extended Kalman Filter

PROBLEM TO BE SOLVED: To obtain a soil improving material that is obtained by effectively utilizing papermaking sludge ash which is conventionally used mostly for reclamation or utilized for a limited use, satisfies necessary strength of demanded soil qualities, prevents elution of heavy metals, controls re-sludging and improves workability and to provide a soil improving method using the soil improving material. SOLUTION: The soil improving material is obtained by combining merits of papermaking sludge ash with a water-soluble polymer compound having cohesive property or a synthetic polymer compound having high water absorption and improves improving effect on soil, prevents elution of heavy metals from soil and brings about prevention effects from re-sludging of improved soil. The soil improving method comprises adding the soil improving material to soil to be treated, kneading and improving workability. COPYRIGHT: (C)2005,JPO&NCIPI

Soil improving material and soil improving method

Identification problem of some seismic systems by extended kalman filter

Sand liquefaction is a very important problem during earthquake excitation. This paper details the development of equivalent linearization theory for sand, based on both the extended Kalman filter and the weighted global iteration method, and shows how this theory can be used to analyze liquefaction. The equivalent linearization model presented in this paper is very simple and numerically efficient. This numerical model for liquefaction allows direct determination of progressive pore‐water pressures. The model requires a relatively small number of parameters in comparison to an effective stress model by Finn et al. (13). The pattern of progressive pore‐water pressure based on the equivalent linearization law agrees reasonably well with Finn's model based on effective stress analysis for the dynamic response of saturated sand. However, the equivalent linearization law is computationally less efficient and a simple tool for evaluating progressive pore‐water pressure during earthquake motions.

Etsuro Saito

Papers

Structural Identification by Extended Kalman Filter

Soil improving material and soil improving method

Identification problem of some seismic systems by extended kalman filter

Linearized Liquefaction Process by Kalman Filter

Estimation of dynamic properties of a multiple degree of freedom linear system