Structure Identification Algorithm Research Articles

Differential Evolution Algorithm for Dynamic Structural Identification

In the present article, differential evolution algorithm is used to perform structural identification of mass and stiffness properties of civil structures from dynamic test results. Identification is performed initially starting from exact values of modal parameters (frequencies and mode shapes). Robustness of the algorithm is then tested by adopting pseudo-experimental input data, obtained by adding to exact data some statistic scattering, representing experimental measurement error. Different objective functions are adopted in identification procedure, and results are compared with those obtained adopting classical gradient method. The method is used to identify masses, elastic moduli, and stiffnesses of external constraints of a RC frame structure and a steel–concrete bridge. Numerical results confirm that adopting both frequencies and mode shapes instead of frequencies only strongly increases sensitivity of objective function to identification parameters. Scattering of identified parameters is much smaller, with coefficient of variation of the same order of magnitude of that of pseudo-experimental data used as input values in dynamic identification procedure.

Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm

Although traditional fuzzy models have proven to have high capacity of approximating the real-world systems, they have some challenges, such as computational complexity, optimization problems, subjectivity, etc. In order to solve some of these problems, this paper proposes a new fuzzy system modeling approach based on improved fuzzy functions to model systems with continuous output variable. The new modeling approach introduces three features: i) an improved fuzzy clustering (IFC) algorithm, ii) a new structure identification algorithm, and iii) a nonparametric inference engine. The IFC algorithm yields simultaneous estimates of parameters of c-regression models, together with fuzzy c-partitioning of the data, to calculate improved membership values with a new membership function. The structure identification of the new approach utilizes IFC, instead of standard fuzzy c-means clustering algorithm, to fuzzy partition the data, and it uses improved membership values as additional input variables along with the original scalar input variables for two different choices of regression methods: least squares estimation or support vector regression, to determine ldquofuzzy functionsrdquo for each cluster. With novel IFC, one could learn the system behavior more accurately compared to other FSM models. The nonparametric inference engine is a new approach, which uses the alike -nearest neighbor method for reasoning. Empirical comparisons indicate that the proposed approach yields comparable or better accuracy than fuzzy or neuro-fuzzy models based on fuzzy rules bases, as well as other soft computing methods.

Implementation algorithms for self-identification of adaptive structures with variable geometric properties

On-orbit vibration testing is one of the issues for in-orbit construction of large space structures because of structural variations during assembly. Variable geometry truss structures would be the candidates as the tools for the in-orbit construction. This paper proposes structural identification algorithms for implementing self-identification of variable geometry truss structures. Using the implementation algorithms, the variable geometry truss determines quasi-static changes of their own geometric parameters to enhance the lack of vibration modes with limited excitation locations for on-orbit vibration testing. The proposed algorithms are numerically examined through the identification of the stiffness matrix of a two-dimensional variable geometry truss with a single variable length member and a single excitation location. We assumed that all the nodes of the truss structure were fully instrumented for measuring mode shape and modal frequency. In the numerical experiments, unknown stiffness matrix of finite element model corresponding to the variable geometry truss is identified under the assumption that exact mass matrix is given using finite element model. It is found that increasing the number of geometry changes can enhance the lack of vibration modes, and the identification error of stiffness matrix is reduced accordingly. The sensitivity of identification accuracy to the modeling error in given mass matrix is also addressed. Furthermore, we discuss a method for selecting sufficient number of geometry changes by considering the convergence of the norm of identified stiffness matrix.

Co‐evolutionary algorithm for structural damage identification using minimal physical testing

AbstractThe problem of damage identification using minimum test data is studied in this work. Data sparsity in damage identification applications commonly results in inverse problems that are mathematically ill‐posed (e.g. non‐unique solutions). Although solution non‐uniqueness may be addressed by performing multiple tests on a structure, it is not trivial to decide which tests to carry out given that actual physical testing is costly. This problem is addressed in this work through a new co‐evolutionary algorithm that interactively searches for damage scenarios and optimum physical tests. The algorithm is composed of two stages: the estimation phase, which searches for damage scenarios that can predict current physical tests, and the exploration phase, which searches for tests that increase the level of information about the damaged system. The feasibility of the methodology is demonstrated using numerical examples. Copyright © 2006 John Wiley & Sons, Ltd.

Algorithm for structural identification DYNAMICS MODELS Multidimensional OBJECT

The new algorithm of dynamic model structural identification of the multivariable stabilized plant with observable and unobservable disturbances in the regular operating modes is offered in this paper. With the help of the offered algorithm it is possible to define the perturbed models of dynamics not only of the plant, but also the dynamics characteristics of observable and unobservable casual disturbances taking into account the absence of low correlation between themselves and control inputs with the unobservable perturbations.

Prediction error method-based second-order structural identification algorithm in stochastic state space formulation

This paper presents a time-domain prediction error method (PEM)-based system identification method for the identification of second-order structural parameters of linear structural systems. The state space model of a linear structure is first expanded into linear AutoRegressive Moving Average with eXogenous excitation (ARMAX) or AutoRegressive Moving Average (ARMA) models by matrix fraction description. Second-order structural parameters are then estimated directly from measured time series data by applying the PEM to ARMAX and ARMA models. The proposed structural identification method can incorporate as much as possible structural information known a priori into the structural identification process to improve the identification accuracy. To avoid the practical difficulty often associated with input measurement, a two-stage structural health monitoring procedure is proposed. Once the mass matrix is identified from the first stage, data collected from ambient vibration survey can be used to identify other structural parameters in the second stage. The effectiveness of the proposed structural identification method as well as the corresponding structural health monitoring method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. The numerical results show that the proposed method is capable of locating and quantifying damages reasonably well in the presence of 10% measurement noise and limited sensor information. Copyright © 2006 John Wiley & Sons, Ltd.

Robust identification for multi-section freeway traffic models

Since it is difficult to fit measured parameters using the conventional traffic model, a new traffic density and average speed model is introduced in this paper. To determine traffic model structures accurately, a model identification method for uncertain nonlinear system is developed. To simplify uncertain nonlinear problem, this paper presents a new robust criterion to identify the multi-section traffic model structure of freeway efficiently. In the new model identification criterion, numerically efficient U-D factorization is used to avoid computing the determinant values of two complex matrices. By estimating the values of U-D factor of data matrix, both the upper and lower bounds of system uncertainties are described. Thus a model structure identification algorithm is proposed. Comparisons between identification outputs and simulation outputs of traffic states show that the traffic states can be accurately predicted by means of the new traffic models and the structure identification criterion.

Algorithm of Dynamic Model Structural Identification of the Multivariable Plant

The approach which tended in this article is based on the theorem of hypotheses (Bayes' formula), which one is intended for definition of the verity of alerts for the automated systems of guards relevant and apart of relevant objects. This method allows to execute synthesizing a system from rather accident-sensitive, but not of expensive subsystems – components.

Structural damage identification algorithm based on changes in power spectral density

Structural damage identification algorithm based on changes in power spectral density

Material identification in structural optimization using response surfaces

This paper evaluates the use of a response surface optimization algorithm for structural material or parameter identification. The algorithm used is the successive response surface method (SRSM) as implemented in LS-OPT. Two methods are used in the formulation of the optimization problem. The first is to minimize the maximum deviation of the distance function between the simulated and experimental results at selected points, while the second approach minimizes the more standard least squares residual form of the distance function, effectively providing a compromised match over all the parameters selected. SRSM uses a trust region that is adapted using a heuristic contraction and panning approach. The method has only one user-required parameter, the size of the initial trust region. To illustrate the robustness of SRSM as a material identification tool, three test cases are presented. The first concerns the identification of the power-law material parameters of a simple tensile test specimen. The second test case determines the leakage coefficient-pressure load curve of an airbag given experimental kinematic data of a chest form impacting the airbag. In the third test case the material identification of a rate-dependent low-density foam material is conducted. It is shown that SRSM essentially converges within 10 iterations for all the test cases, and that the two distance function minimization approaches produce similar results.

The structural identification method of the dynamic plant by experimental data

The common method, task and algorithm of structural identification of multidimensional stabilizable plant dynamic models under the action of controlling, controlled and uncontrolled during the experiments stochastic disturbances are offered. The common and main special cases of identification task are set and the mathematical algorithms of their solving are given.

Algorithm of Dynamic Model Structural Identification of the Multivariable Plant

The new algorithm of dynamic model structural identification of the multivariable stabilized plant with observable and unobservable disturbances in the regular operating modes is offered in this paper. With the help of the offered algorithm it is possible to define the “perturbed” models of dynamics not only of the plant, but also the dynamics characteristics of observable and unobservable casual disturbances taking into account the absence of correlation between themselves and control inputs with the unobservable perturbations.

Algorithm of Dynamic Model Structural Identification of the Multivariable Plant

The necessity of development capacitor transformer of geometrical height for automation of flight at small heights is shown. The approach to statement of an electrostatic task of modeling capacitor transformer of geometrical height is considered. The technique of account of potentials of an electrostatic field is offered, and also technique of account of charges on electrodes capacitor transformer the algorithm of the decision of the put task Is submitted. As an example the result of account of a potential field capacitor transformer of geometrical height as a picture of force lines are given.

Algorithm of Dynamic Model Structural Identification of the Multivariable Plant

3d Max programming environment peculiarities for constructing 3d wire-grid models of electrodynamical objects have been considered.

Genetic programming for model selection of TSK-fuzzy systems

This paper compares a genetic programming (GP) approach with a greedy partition algorithm (LOLIMOT) for structure identification of local linear neuro-fuzzy models. The crisp linear conclusion part of a Takagi-Sugeno-Kang (TSK) fuzzy rule describes the underlying model in the local region specified in the premise. The objective of structure identification is to identify an optimal partition of the input space into Gaussian, axis-orthogonal fuzzy sets. The linear parameters in the rule consequent are then estimated by means of a local weighted least-squares algorithm. LOLIMOT is an incremental tree-construction algorithm that partitions the input space by axis-orthogonal splits. In each iteration it greedily adds the new model that minimizes the classification error. GP performs a global search for the optimal partition tree and is therefore able to backtrack in case of sub-optimal intermediate split decisions. We compare the performance of both methods for function approximation of a highly non-linear two-dimensional test function and an engine characteristic map.

Structural damage identification using static test data and changes in frequencies

A structural damage identification algorithm using static test data and changes in natural frequencies is presented in this paper. To locate damage in the structure, the Damage Signature Matching (DSM) technique is improved through a proper definition of Measured Damage Signatures (MDS) and Predicted Damage Signatures (PDS). The effect of damage severity can be eliminated effectively as the result that the first-order approximation of changes in static deformation and natural frequencies are employed jointly in the damage signatures. The damage location, then, can be detected successfully by matching MDS with PDS. After obtaining the possible damage location, an iterative estimation scheme for solving non-linear optimization programming problems, which is based on the quadratic programming technique, is proposed to predict the damage extent. A remarkable characteristic of the present approach is that it can be directly applied in the cases of incomplete measured data. The correctness and the effectiveness of the algorithm are proved by two examples: A planar truss model with the numerically simulated data and a beam with two-fixed ends, of which the static response and the natural frequencies are obtained experimentally. The results show that the proposed algorithm is efficient for the damage identification.

Structural Identification of Nonlinear Dynamic Objects in the Conditions of a Passive Experiment

A structural identification algorithm for nonlinear block-oriented models of the Hammerstein type and the Zadeh filter is considered. The algorithm for structural identification relies on the narrow-band probing of a nonlinear object to the input of which test signals cannot be fed. Frequency-time criteria are listed, with the aid of which the structures of adequate models are defined.

Problematic questions and research-capacious technologies of structural identification of objects dynamic models according to full-scale investigation

Proposed is an algorithm of structural identification of objects dynamic models according to natural Investigation data. Worked out is methodics of making structural identification with natural experiment. Determined are the requirements to picking up and processing experimental information. Worked out are programs for solution of problems of structural identification on personal computer. To illustrate the efficiency of structural identification algorithm we have chosen identification model of three dimensional dynamic stand as an example

Problematic questions of block primary measurement attestation of БИНС on dynamic simulator of airplane angular motion

Described are peculiarities of dynamic attestation of block transducers of angular velocity and accelerometers as multivariable system. Presented is the stand diagram for attestation of block trans­ducers also are proposed regularized spectmm algorithm of structural identification of block trans­ducers and their obstacls

Frequency Domain Structural System Identification by Observability Range Space Extraction

This paper presents the Frequency Domain Observability Range Space Extraction (FORSE) identification algorithm. FORSE is a singular value decomposition based identification algorithm which constructs a state space model directly from frequency domain data. The concept of system identification by observability range space extraction was developed by generalizing the Q-Markov Covariance Equivalent Realization and Eigensystem Realization Algorithm. The numerical properties of FORSE are well behaved when applied to multi-variable and high dimensional structural systems. It can achieve high modeling accuracy by properly overparameterizing the system. The effectiveness of this algorithm for structural system identification is demonstrated using the MIT Middeck Active Control Experiment (MACE). MACE is an active structural control experiment to be conducted in the Space Shuttle middeck. Results of ground experiments using this algorithm will be discussed.