An inverse method for the estimation of input forces acting on non-linear structural systems

Ma, Chih-Kao; Ho, Chih-Chergn

doi:10.1016/s0022-460x(03)00797-1

Cited by 72 publications

(33 citation statements)

References 12 publications

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“…Load identification has been in focus in the recent decade, and many attempts have been made in order to obtain a stable and accurate method. In recent years, identification techniques in real time using Kalman filters have proven successful in certain cases [2][3][4][5][6][7]. Besides the recursive model description, another benefit from these methods is the capability to merge different sensor type information.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Effect of Modelling Errors in Load Identification Using Classical Identification Methods

Vigsø

Brincker

Georgakis

2019

Shock and Vibration

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Load identification, or input identification as the more general term, is a field of study that requires a wide set of disciplines, which suffers from uncertainties caused by the challenges within each discipline. When making load identification, several different approaches exist. For all (or at least most) methods, however, some sort of system model is required. This model may be simple or complex, depending on the system at hand. Typically, if the identification process is vibration fed, the system model will be created from modal parameters. These parameters, however, are often subject to uncertainty and thus may be considered as stochastic variables. In this paper, the root causes of uncertainty for load identification are demonstrated using classical identification techniques. From a numerical perspective, uncertainty is quantified through Monte Carlo simulations. Two results are outlined: one where the identification process is completely blindfolded in its most naive form, and one where the spatial distribution of the load is predefined. In general, it is found that fixing the spatial distribution of the load can compensate for truncation errors in the modal parameters.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Effect of Modelling Errors in Load Identification Using Classical Identification Methods

Vigsø

Brincker

Georgakis

2019

Shock and Vibration

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“…An extended inverse estimation algorithm based on the extended Kalman filtering (EKF) and recursive least squares methods has been developed by Lee and Liu to estimate the unknown input load applied on a nonlinear SDOF model representing a tower structure. Ma et al presented an inverse method to identify input forces in structural models using the EKF method. Their method has been validated using a nonlinear MDOF mass–spring–dashpot model and a linear elastic cantilever beam model.…”

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

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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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“…This method requires a full state measurement, which might not be feasible in practice. Later, Ma and Ho (2004) extended their previous works to nonlinear systems by using an extended Kalman filter in conjunction with the least-squares estimator. In recent years, Xu et al (2010) used an extended Kalman filter to evaluate the impacting pressure loads on a structure.…”

Section: Introductionmentioning

confidence: 99%

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

Zhi

Ming-xin

2016

Computer aided Civil Eng

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This article presents a Kalman‐filter‐based estimation algorithm for identification of wind loads on a super‐tall building using limited structural responses. In practice, acceleration responses are most convenient to be measured among wind‐induced dynamic responses of structures. The proposed inverse method allows estimating the unknown wind loads and structural responses of a super‐tall building using limited acceleration measurements. Taipei 101 Tower is a super‐tall building with 101 stories and a height of 508 m. Field measurements and numerical simulations of the wind effects on Taipei 101 Tower are conducted. The wind loads acting on the super‐tall building are estimated based on the wind‐induced responses determined from the numerical simulations and the refined finite‐element model of the structure, which are in good agreement with the exact results. The stability performance of the proposed algorithm is evaluated. The influence of noise levels in the measurements and covariance matrix of noise on the identification accuracy are investigated and discussed based on the L‐curve method. Finally, the wind loads and structural responses are reconstructed based on the field‐measured accelerations during Typhoon Matsa. The accuracy of the identified results is verified by comparing the reconstructed acceleration responses with the field measurements. The results of this study show that the proposed inverse approach can provide accurate predictions of the wind loads and wind‐induced responses of super‐tall buildings based on limited measured responses.

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An inverse method for the estimation of input forces acting on non-linear structural systems

Cited by 72 publications

References 12 publications

Evaluating the Effect of Modelling Errors in Load Identification Using Classical Identification Methods

Evaluating the Effect of Modelling Errors in Load Identification Using Classical Identification Methods

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

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

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