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Hybrid simulations that combine numerical computations and physical experiment represent an effective method of evaluating the dynamic response of structures. However, it is sometimes impossible to take all the uncertain or nonlinear parts of the structure as the physical substructure. Thus, the modeling errors of the numerical part can raise concerns. One method of solving this problem is to update the numerical model by estimating its parameters from experimental data online. In this paper, an online model updating method for the hybrid simulation of frame structures is proposed to reduce the errors of nonlinear modeling of numerical substructures. To obtain acceptable accuracy with acceptable extra computation efforts as a result of model parameter estimation, the sectional constitutive model is adopted, therein considering axial‐force and bending‐moment coupling; moreover, the unscented Kalman filter is used for parameter estimation of the sectional model. The effectiveness of the sectional model updating with the unscented Kalman filter is validated via numerical analyses and actual hybrid tests on a full‐scale steel frame structure, with one column as the experimental substructure loaded by three actuators to guarantee the consistency of the boundary conditions. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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In this study, we formulate an improved finite element model‐updating method to address the numerical difficulties associated with ill conditioning and rank deficiency. These complications are frequently encountered model‐updating problems, and occur when the identification of a larger number of physical parameters is attempted than that warranted by the information content of the experimental data. Based on the standard bounded variables least‐squares (BVLS) method, which incorporates the usual upper/lower‐bound constraints, the proposed method (henceforth referred to as BVLSrc) is equipped with novel sensitivity‐based relative constraints. The relative constraints are automatically constructed using the correlation coefficients between the sensitivity vectors of updating parameters. The veracity and effectiveness of BVLSrc is investigated through the simulated, yet realistic, forced‐vibration testing of a simple framed structure using its frequency response function as input data. By comparing the results of BVLSrc with those obtained via (the competing) pure BVLS and regularization methods, we show that BVLSrc and regularization methods yield approximate solutions with similar and sufficiently high accuracy, while pure BVLS method yields physically inadmissible solutions. We further demonstrate that BVLSrc is computationally more efficient, because, unlike regularization methods, it does not require the laborious a priori calculations to determine an optimal penalty parameter, and its results are far less sensitive to the initial estimates of the updating parameters. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation 总被引：1，自引：0，他引：1
Haishen Lü Zhongbo Yu Yonghua ZhuSam Drake Zhenchun HaoEdward A. Sudicky 《Advances in water resources》2011,34(3):395-406
With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards’ equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect. 相似文献
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结合随机状态空间方程和极大似然法的期望最大EM算法进行了结构运行模态分析。EM算法以迭代的方式更新模型参数,进而得到状态空间方程的极大似然估计。模态参数通过状态空间模型参数求得。应用了平方根卡尔曼滤波方程提高EM迭代过程的计算稳健性。考虑到状态空间方程中激励噪声和测量噪声的相关性,建立了更完善的参数化状态空间方程。通过数值模拟对比分析,结果表明：考虑噪声相关性的EM算法比假设噪声不相关的EM算法具有更高的识别精度,EM算法在采样数据较少的情况下比随机子空间方法更有优势。 相似文献
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Traditional Ensemble Kalman Filter (EnKF) data assimilation requires computationally intensive Monte Carlo (MC) sampling, which suffers from filter inbreeding unless the number of simulations is large. Recently we proposed an alternative EnKF groundwater-data assimilation method that obviates the need for sampling and is free of inbreeding issues. In our new approach, theoretical ensemble moments are approximated directly by solving a system of corresponding stochastic groundwater flow equations. Like MC-based EnKF, our moment equations (ME) approach allows Bayesian updating of system states and parameters in real-time as new data become available. Here we compare the performances and accuracies of the two approaches on two-dimensional transient groundwater flow toward a well pumping water in a synthetic, randomly heterogeneous confined aquifer subject to prescribed head and flux boundary conditions. 相似文献
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This paper presents two methods to perform system identification at the substructural level, taking advantage of reduction in the number of unknowns and degrees of freedom (DOFs) involved, for damage assessment of fairly large structures. The first method is based on first‐order state space formulation of the substructure where the eigensystem realization algorithm (ERA) and the observer/Kalman filter identification (OKID) are used. Identification at the global level is then performed to obtain the second‐order model parameters. In the second method, identification is performed at the substructural level in both the first‐ and second‐order model identification. Both methods are illustrated using numerical simulation studies where results indicate their significantly better performance than identification using the global structure, in terms of efficiency and accuracy. A 12‐DOF system and a fairly large structural system with 50 DOFs are used where the effects of noisy data are considered. In addition to numerical simulation studies, laboratory experiments involving an eight‐storey frame model are carried out to illustrate the performance of the proposed method. The identification results presented in terms of the stiffness integrity index show that the proposed methodology is able to locate and quantify damage fairly accurately. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
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A substructure approach is used to estimate the stiffness and damping coefficients of structures from measurement of dynamic responses. The structures are decomposed into smaller subsystems for which state and observation equations are formulated and solved by the method of extended Kalman filter with a weighted global iteration algorithm. Substructural identification methods with and without overlapping members are proposed. In both methods, the convergence of the structural parameters to the optimal values is improved significantly with less computation time as compared to a complete structural approach. Numerical simulation studies are performed for three types of structures, namely a shear building, a plane frame building and a plane truss bridge. The effects of measurement noise and response observations required for identification of system parameters are also investigated. 相似文献
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An extended Kalman filter algorithm with local iteration is presented for the identification of non-linear and non-stationary soil properties. Borehole-array strong motions were recorded at a liquefied site during the 1995 Hyogoken-nanbu earthquake. In this study, a modified Kalman filtering method in which the extended Kalman filter is iteratively used at every local time-step to track rapid parameter changes is proposed. The method is then applied to the instrumented soil layer, which is modeled by an equivalent linear model. An identification of non-linear and non-stationary soil properties was conducted successfully; and non-linear restoring force–displacement relationships including progression with time were obtained. 相似文献
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Substructure hybrid simulation has been actively investigated and applied to evaluate the seismic performance of structural systems in recent years. The method allows simulation of structures by representing critical components with physically tested specimens and the rest of the structure with numerical models. However, the number of physical specimens is limited by available experimental equipment. Hence, the benefit of the hybrid simulation diminishes when only a few components in a large system can be realistically represented. The objective of the paper is to overcome the limitation through a novel model updating method. The model updating is carried out by applying calibrated weighting factors at each time step to the alternative numerical models, which encompasses the possible variation in the experimental specimen properties. The concept is proposed and implemented in the hybrid simulation framework, UI‐SimCor. Numerical verification is carried out using two‐DOF systems. The method is also applied to an experimental testing, which proves the concept of the proposed model updating method. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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随机子空间识别方法计算效率的改进 总被引：4，自引：0，他引：4
参数识别是结构健康监测领域研究中的重点。随机子空间法是近年来发展起来的一种线性系统辩识方法,可以有效地从环境激励的结构响应中获取模态参数。该方法的基本原理是将“将来”数据向“过去”数据进行垂直投影,进而根据该投影计算出可观测矩阵和一个Kalman滤波状态序列。而Kalman滤波序列是“过去”输出信号的线性组合,即“过去”输出和“将来”状态估计建立了关系。而从Hankel矩阵的组成来看,由于要使得该矩阵具有单无限的条件,所需的计算时间也比较长。据此对随机子空间方法进行了改进。改进的基本思想是采用一个测点的信号作为“过去”作为输出信号代替全部测点的信号。这样就减少了计算量。最后用一数值模拟算例进行了验证,结果良好。 相似文献
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Structural identification based on analysis of vibration signals is usually formulated as an optimization problem. Nevertheless, identifying a structural system as a whole often involves a great number of unknowns and degrees of freedom, resulting in considerable convergence difficulty and expensive evaluation of objective function. To handle these issues, this paper aims to propose a frequency domain substructural identification method applicable to arbitrary excitations. This is done by introducing the exponential window method in the formulation so that the effect caused by initial condition is alleviated without using zero padding. The proposed identification method is verified through both numerical and experimental studies. It is shown that significant reduction in computer time can be achieved in addition to remarkable identification accuracy. The experimental study also indicates that the proposed strategy is effective in identifying small structural changes. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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Monitoring crustal movements is important in understanding the earth surface and in developing theories about plate tectonics.
Plate tectonics describes earth crust which consists of a number of plates moving relative to one another. Global plate models
suggest that plate movements are constant due to the fact that relative movements of plates were represented by averaged plate
motion. However, if an earthquake occurs, the uniform movement of related plates does not follow the regular trend.
In analysis by Kalman Filter, the effects of an earthquake occured within crustal movement monitoring period by geodetic method
have been eliminated by a proposed approach, Fading Memory Filter. The proposed method was tested using a real/simulated data.
The results of the test showed that the effect of a strong earthquake (Mw = 7.1) which had occurred in the Gulf of Eilat/Aqaba was eliminated. Consequently, the proposed method is capable of removing
or eliminating a suddan effect in monitoring crustal movements in the analysis by using the Kalman Filter. 相似文献
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基于复模态的有限元模型修正算法 总被引：2，自引：0，他引：2
针对地下结构地震响应分析中无限地基辐射阻尼问题，引入复模态情况下的具有非简化的堆积阻尼矩阵的阻尼模型，并针对具有集中质量阵的阻尼模型提出了合并与质量有关的阻尼和堆积阻尼的思想，并据此提出了一种修正此类有限元模型的两步法，首先从复模态参数中提取实模态参数，采用基于模态残余力的识别算法修正刚度矩阵，然后根据复模态参数和已得的刚度矩阵来识别阻尼模型中的刚度参与系数和质量阻尼堆积阻尼联合矩阵。 相似文献
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This paper deals with the identification of the parameters of a smoothed hysteretic model which was proposed by Bouc and Wen with emphasis on restoring force hysteresis. The problem of estimating the parameters of this system on the basis of input-output data, possibly noise corrupted, is considered. Through the application of various simulated time histories from the hysteretic model, a three-stage systematic method of system identification was proposed. Four different methods of identification are arranged and conducted in this three-stage system identification. The first stage, a sequential regressional analysis is used to identify the equivalent linear system from which elastic or inelastic response can be identified. The identified parameters can be used in the stage when the system is in elastic response. In the second stage, both time domain least-squares method and Gauss-Newton method are applied. The convergence of the Gauss-Newton method can be guaranteed if the identified results from least-squares method are adopted as the initial values for Gauss-Newton method. In the third stage, the extended Kalman filtering technique is needed to identify the noise-corrupt data. Application of this algorithm to a SDOF non-deteriorating system is verified. 相似文献
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Extended Kalman filter for material parameter estimation in nonlinear structural finite element models using direct differentiation method 
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下载免费PDF全文 This paper presents a novel nonlinear finite element (FE) model updating framework, in which advanced nonlinear structural FE modeling and analysis techniques are used jointly with the extended Kalman filter (EKF) to estimate time‐invariant parameters associated to the nonlinear material constitutive models used in the FE model of the structural system of interest. The EKF as a parameter estimation tool requires the computation of structural FE response sensitivities (total partial derivatives) with respect to the material parameters to be estimated. Employing the direct differentiation method, which is a well‐established procedure for FE response sensitivity analysis, facilitates the application of the EKF in the parameter estimation problem. To verify the proposed nonlinear FE model updating framework, two proof‐of‐concept examples are presented. For each example, the FE‐simulated response of a realistic prototype structure to a set of earthquake ground motions of varying intensity is polluted with artificial measurement noise and used as structural response measurement to estimate the assumed unknown material parameters using the proposed nonlinear FE model updating framework. The first example consists of a cantilever steel bridge column with three unknown material parameters, while a three‐story three‐bay moment resisting steel frame with six unknown material parameters is used as second example. Both examples demonstrate the excellent performance of the proposed parameter estimation framework even in the presence of high measurement noise. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a sufficiently large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos expansion (PCE) to represent and propagate the uncertainties in parameters and states. However, PCKF suffers from the so-called “curse of dimensionality”. Its computational cost increases drastically with the increasing number of parameters and system nonlinearity. Furthermore, PCKF may fail to provide accurate estimations due to the joint updating scheme for strongly nonlinear models. Motivated by recent developments in uncertainty quantification and EnKF, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected at each assimilation step; the “restart” scheme is utilized to eliminate the inconsistency between updated model parameters and states variables. The performance of RAPCKF is systematically tested with numerical cases of unsaturated flow models. It is shown that the adaptive approach and restart scheme can significantly improve the performance of PCKF. Moreover, RAPCKF has been demonstrated to be more efficient than EnKF with the same computational cost. 相似文献