Material Parameter Identification in Distributed Plasticity FE Models of Frame-Type Structures Using Nonlinear Stochastic Filtering

Astroza, Rodrigo; Ebrahimian, Hamed; Conte, Joseph Le

doi:10.1061/(asce)em.1943-7889.0000851

Cited by 101 publications

(55 citation statements)

References 37 publications

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“…Different frameworks for mechanics‐based nonlinear FE model updating have been developed recently . These frameworks provide an advanced approach for system and damage ID and SHM of civil structures, which overcome the important shortcomings of the damage ID methods based on linear modal ID.…”

Section: Introductionmentioning

confidence: 99%

“…This paper provides a novel framework for output‐only (or blind) mechanics‐based nonlinear structural FE model updating and damage ID of civil structures using sparsely measured response of the structure to earthquake excitation. This framework is built upon an existing nonlinear FE model updating approach using both input and output dynamic measurement data, which was developed by the authors . In the proposed framework, the unknown FE model parameters and the ground acceleration time histories are estimated jointly, using only the sparsely measured dynamic response of the structure.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures

Ebrahimian

Astroza

Conte

et al. 2018

Struct Control Health Monit

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Summary This paper presents a new framework for output‐only nonlinear system and damage identification of civil structures. This framework is based on nonlinear finite element (FE) model updating in the time‐domain, using only the sparsely measured structural response to unmeasured or partially measured earthquake excitation. The proposed framework provides a computationally feasible approach for structural health monitoring and damage identification of civil structures when accurate measurement of the input seismic excitations is challenging (e.g., buildings with significant foundation rocking and bridges with piers in deep water) or the measured seismic excitations are erroneous and/or distorted by significant measurement error (e.g., malfunctioning sensors). Grounded on Bayesian inference, the proposed framework estimates the unknown FE model parameters and the ground acceleration time histories simultaneously, using the sparse measured dynamic response of the structure. Two approaches are presented in this study to solve the joint structural system parameter and input identification problem: (a) a sequential maximum likelihood estimation approach, which reduces to a sequential nonlinear constrained optimization method, and (b) a sequential maximum a posteriori estimation approach, which reduces to a sequential iterative extended Kalman filtering method. Both approaches require the computation of FE response sensitivities with respect to the unknown FE model parameters and the values of base acceleration at each time step. The FE response sensitivities are computed efficiently using the direct differentiation method. The two proposed approaches are validated using the seismic response of a 5‐story reinforced concrete building structure, numerically simulated using a state‐of‐the‐art mechanics‐based nonlinear structural FE modeling technique. The simulated absolute acceleration response time histories of 3 floors and the relative (to the base) roof displacement response time histories of the building to a bidirectional horizontal seismic excitation are polluted with artificial measurement noise. The noisy responses of the structure are then used to estimate the unknown FE model parameters characterizing the nonlinear material constitutive laws of the concrete and reinforcing steel and the (assumed) unknown time history of the ground acceleration in the longitudinal direction of the building. The same nonlinear FE model of the structure is used to simulate the structural response and to estimate the dynamic input and system parameters. Thus, modeling uncertainty is not considered in this paper. Although the validation study demonstrates the estimation accuracy of both approaches, the sequential maximum a posteriori estimation approach is shown to be significantly more efficient computationally than the sequential maximum likelihood estimation approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures

Ebrahimian

Astroza

Conte

et al. 2018

Struct Control Health Monit

Self Cite

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“…A suitable mathematical model for civil structures is very important for health monitoring and structural control. For structural health monitoring, if a simple and accurate model can be obtained based on the acquired data, then future structural behaviors can be predicted by this model, and some possible damages or failures may be avoided . The effects of structural control depend on the structural model, even for intelligent control methods .…”

Section: Introductionmentioning

confidence: 99%

An UKF-based nonlinear system identification method using interpolation models and backward integration

Qin

2018

Struct Control Health Monit

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SummaryIn this paper, a novel identification method for nonlinear systems is proposed.This method utilizes linear interpolation models to describe the nonlinear forces of the physical models, and the unscented Kalman filter (UKF) method is adopted for the task of nonlinear identification. With the help of a linear interpolation algorithm, the proposed method requires little prior knowledge of the form of the nonlinear stiffness. Therefore, this method takes advantage of both the independence of the linear interpolation points and the inherent mathematical properties of the UKF. The UKF method is also modified to better fit the needs of parameter identification. To further emphasize parameter identification, backward integration and observations of the previous states are used. Two numerical simulations of the nonlinear elastic stiffness and Bouc-Wen hysteresis are conducted to show the flexibility and efficiency of this method. In these 2 examples, the observation signals are generated by analytic models, and the identifications are conducted with a linear interpolation model. | INTRODUCTIONA suitable mathematical model for civil structures is very important for health monitoring and structural control. For structural health monitoring, if a simple and accurate model can be obtained based on the acquired data, then future structural behaviors can be predicted by this model, and some possible damages or failures may be avoided. [1][2][3][4] The effects of structural control depend on the structural model, even for intelligent control methods. [5][6][7][8] The more accurate a model that can be obtained, the better control effect can be realized. In a civil structural model, nonlinearity commonly exists with respect to health monitoring and structural control. Simply assuming linearity of the structure behavior will cause significant error. To describe a particular nonlinear behavior, there are usually plenty of proper mathematical models available. Examples include the polynomial model [9,10] and multilinear model [11] for elastic stiffness, BoucWen model, [12] Preisach model, [13] and Iwan model [14] for hysteretic force. Different models fit for different problems.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Figure 4 summarizes the algorithm to update the nonlinear FE model using the UKF. More information on the nonlinear FE model updating using the EKF and UKF can be found in [8] and [7], respectively. It is noted that in Figures 2, 3, and 4…”

Section: -P2mentioning

confidence: 99%

“…However, such models are not commonly used in state-of-the-art mechanics-based structural FE modeling because they cannot adequately simulate the actual nonlinear behavior of civil structures. Recently, the problem of updating mechanics-based nonlinear FE models of structures using input-output data recorded during damage-inducing events has been investigated [6][7][8]. The main benefit of using advanced FE models is that they are able to capture the damage mechanisms to be detected and identified in the structural system and therefore can provide accurate information about presence, location, type, and extent of damage in the structure.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Methods for Nonlinear System Identification of Civil Structures

Conte

Astroza

Ebrahimian

2015

MATEC Web of Conferences

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Abstract. This paper presents a new framework for the identification of mechanics-based nonlinear finite element (FE) models of civil structures using Bayesian methods. In this approach, recursive Bayesian estimation methods are utilized to update an advanced nonlinear FE model of the structure using the input-output dynamic data recorded during an earthquake event. Capable of capturing the complex damage mechanisms and failure modes of the structural system, the updated nonlinear FE model can be used to evaluate the state of health of the structure after a damage-inducing event. To update the unknown time-invariant parameters of the FE model, three alternative stochastic filtering methods are used: the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the iterated extended Kalman filter (IEKF). For those estimation methods that require the computation of structural FE response sensitivities with respect to the unknown modeling parameters (EKF and IEKF), the accurate and computationally efficient direct differentiation method (DDM) is used. A three-dimensional five-story two-by-one bay reinforced concrete (RC) frame is used to illustrate the performance of the framework and compare the performance of the different filters in terms of convergence, accuracy, and robustness. Excellent estimation results are obtained with the UKF, EKF, and IEKF. Because of the analytical linearization used in the EKF and IEKF, abrupt and large jumps in the estimates of the modeling parameters are observed when using these filters. The UKF slightly outperforms the EKF and IEKF.

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Material Parameter Identification in Distributed Plasticity FE Models of Frame-Type Structures Using Nonlinear Stochastic Filtering

Cited by 101 publications

References 37 publications

Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures

Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures

An UKF-based nonlinear system identification method using interpolation models and backward integration

Bayesian Methods for Nonlinear System Identification of Civil Structures

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