Identification Of Structural Parameters Research Articles

Efficient Inverse Method for Structural Identification Considering Modeling and Response Uncertainties

The inverse problem analysis method provides an effective way for the structural parameter identification. However, uncertainties wildly exist in the practical engineering inverse problems. Due to the coupling of multi-source uncertainties in the measured responses and the modeling parameters, the traditional inverse method under the deterministic framework faces the challenges in solving mechanism and computing cost. In this paper, an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identification of unknown structural parameters according to the uncertain measured responses and modeling parameters. Firstly, the polygonal convex set model is established to quantify the epistemic uncertainties of modeling parameters. Afterwards, a space collocation method based on dimension reduction decomposition is proposed to transform the inverse problem considering multi-source uncertainties into a few interval inverse problems considering response uncertainty. The transformed interval inverse problem involves the two-layer solving process including interval propagation and optimization updating. In order to solve the interval inverse problems considering response uncertainty, an efficient interval inverse method based on the high dimensional model representation and affine algorithm is further developed. Through the coupling of the above two strategies, the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure, and then effectively realizes the uncertainty identification of unknown structural parameters. Finally, two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.

Parameter Identification of Structures with Different Connections Using Static Responses

This paper presents a parameter-identification method for rod structures with different connections. In this method, the parameters of the structure are adjusted to match its analytical and measured displacements. The damage identification for truss structure and rigid frame were investigated. Previous studies often considered the cross-sectional area damage or joint damage; there are few studies on the simultaneous existence of these two types of damage. In this study, damage identification for a rigid frame with both cross-sectional and joint damage was performed. Based on the measured displacements, the proposed method can accurately identify the cross-sectional and joint damage for a rigid frame.

МОДЕЛЮВАННЯ ЕЛЕКТРОСПОЖИВАННЯ ЕЛЕКТРОТЕХНІЧНОГО КОМПЛЕКСУ СИСТЕМИ ВОДОПОСТАЧАННЯ

The article is devoted to solving the issue of energy consumption planning of complex energy technological complexes. The expediency of application of self-organization methods of models of complex systems for modeling of power consumption based on the principles of inductive modeling was substantiated. Power consumption modeling was performed using neural networks based on iterative algorithms of the group method of data handling. The use of GMDH neural networks provided automatic structural-parametric identification of the model. The search for the optimal structure of the model was performed in three classes of support functions of neural networks. As a result of the algorithm of synthesis of models, the set of models-candidates having optimum structure in its class was formed. The obtained models have high modeling quality and low forecast error. Complicating the structure of the neural network does not significantly improve the quality of modeling results. The selection of the best structure of the model was made from the standpoint of multicriteria selection using a morphometric approach. Previously, all the criteria were reduced to one type according to the function of the goal and the procedure of their normalization was performed. The best structure of the model was chosen taking into account the area of the figure formed by the normalized values of the selection criteria. The polynomial GMDH neural network of the first order was chosen as the power consumption model. Deviations of actual and model values of power consumption do not exceed 1%. The usage of GMDH neural networks as an algorithm for model synthesis, supplemented by multi-criteria selection of the best model structure, allowed making an unambiguous choice of the structure of the power consumption model, suitable for determining the energy baseline.

Enhanced Cellular Materials through Multiscale, Variable-Section Inner Designs: Mechanical Attributes and Neural Network Modeling.

In the current work, the mechanical response of multiscale cellular materials with hollow variable-section inner elements is analyzed, combining experimental, numerical and machine learning techniques. At first, the effect of multiscale designs on the macroscale material attributes is quantified as a function of their inner structure. To that scope, analytical, closed-form expressions for the axial and bending inner element-scale stiffness are elaborated. The multiscale metamaterial performance is numerically probed for variable-section, multiscale honeycomb, square and re-entrant star-shaped lattice architectures. It is observed that a substantial normal, bulk and shear specific stiffness increase can be achieved, which differs depending on the upper-scale lattice pattern. Subsequently, extended mechanical datasets are created for the training of machine learning models of the metamaterial performance. Thereupon, neural network (NN) architectures and modeling parameters that can robustly capture the multiscale material response are identified. It is demonstrated that rather low-numerical-cost NN models can assess the complete set of elastic properties with substantial accuracy, providing a direct link between the underlying design parameters and the macroscale metamaterial performance. Moreover, inverse, multi-objective engineering tasks become feasible. It is shown that unified machine-learning-based representation allows for the inverse identification of the inner multiscale structural topology and base material parameters that optimally meet multiple macroscale performance objectives, coupling the NN metamaterial models with genetic algorithm-based optimization schemes.

Structural Parametric Identification of Linear Dynamic Objects

The problem of structural-parametric identification of linear stationary dynamic objects under conditions of active experiments is considered. The question of the adequacy of dynamic models based on the analysis of the object’s response to a typical test impact is investigated. These studies allow us to formulate an important methodological aspect that is recommended to be taken into account in new developments of active identification algorithms. In this paper it is shown that in real conditions of interference, the classical methodology of constructing the transfer function of an object by means of the analysis of data single experiment, i.e. the response to a test effect can give incorrect results. In this regard, another approach is proposed — to form the base of the experimental data starting from a series of identification experiments, and because of its to investigate the dynamic structure of free and forced movements of the object. The ideological basis of this approach consists of the following solutions: the principle of " probing" the space of states of an object in order to determine its dynamic order; the mechanism of excitation of free movements in the object by means of finite control actions; the integral-operator form of representation of the dynamics of the object, which avoids the need for differentiation of input and output signals; the apparatus of linear regression for parametric identification of the transfer function of the object. One the base the given approach a new method of structural-parametric identification of linear stationary dynamic objects is developed and investigated. The presented results and computer approbation in the MATLAB environment have shown the operability and effectiveness of this identification method.

Study on modal parameter identification of engineering structures based on nonlinear characteristics

Abstract To study the nonlinear characteristics of the modal recognition of civil engineering parameters, a method of nonlinear recognition of the parameters of characteristics based on LMD is proposed. The LMD method is applied to decompose the acceleration response signals of the disturbing structure of the building, to obtain the PF components, the instantaneous frequency, and the instantaneous amplitude of each PF component, to determine the modal natural frequency and damping coefficient. To determine the modal parameter based on the LMD, the calculation and analysis results are presented as follows: the frequency of the components fluctuates between the fifth and sixth models, which shows that the components contain the reaction of the fifth and sixth design modes. This is because these two modes (3.101 Hz and 3.147 Hz) are very close to each other, which makes it difficult to distinguish between the responses of these two modes by the LMD method. The frequency of the components is always stable (the first 2.5 s), which indicates that during this period the responses of modes 5e and 6e do not dampen, and the ratio between them in the PF1 components does not differ much. The component frequency curve shows an interesting phenomenon. Starting from about 3.8 s, the frequency curve gradually approaches the first mode, and only the frequency of the first mode is about 6 s, which indicates that the response of the first mode still exists and makes up a significant proportion. Modular response, caused by the damping, is only detected in the first half of the 10 s response, after which it is verified from the nonlinear characteristics of the LMD parameter recognition method that half of the third-order modal response on the scale is very low and almost equal to zero, and despite problems with dense frequency separation mode in the LMD method, the frequency responses of its PF components may reflect the mode combination phenomenon and reflect the duration of each mode throughout the response.

Application of the method of structural-parametric identification to determine the mathematical model of a multidimensional object with distributed parameters and delay

Drying is one of the most responsible and energy-intensive post-harvest grain processing operations. The constant rise in heat prices has led to the search for new designs of grain dryers, or improvements to existing ones. Studies of well-known scientists such as Romankov, Rezchikov, Rashkovskaya have proven the potential effectiveness of using a fluidized bed for drying grain. The degree to which this drying efficiency approaches the potential is determined by how much the change in the actual humidity of the material during drying approaches the theoretical drying curve at the same resource consumption and the same initial state of the material to be dried. Taking into account the provisions of the theory of optimal control indicates that max-imizing the accuracy of stabilization of a given value of the height of the fluidized bed should be achieved by developing an optimal multidimensional stabilization system of the specified height. The basis for solving the first of these is the study of the design of a fluidized bed grain dryer. The decomposition of the grain dryer allows to present it in the form of a set of multidi-mensional objects with pure delay. The size of this set is determined by the number of cascades, and the dynamic characteristics of its elements depend on the distance of the cascade to the gate Δ. Automation of control and management of processes of processing and storage of grain opens wide opportunities for increase of efficiency of use, increase of productivity of current lines, further reduction of labor costs and improvement of quality of processing of grain. If today most of the known dryers have their own mathematical model, this fluidized bed dryer is new, incompletely studied and therefore needs its own mathematical description in order to develop a system of automatic control of its parameters. Therefore, this paper shows the practical application of the technique of identification of signals "input-output" of the dryer for the dispersed material of the cascade type. According to the spectral and mutual spectral densities of the signals, using the technology of structural-parametric identification, a matrix of transfer functions of the dryer was found, which is a multidimensional dynamic object with distributed parameters with delay, which is affected by various perturbing factors.

Simultaneous identification of structural damage and nonlinear hysteresis parameters by an evolutionary algorithm-based artificial neural network

An evolutionary algorithm-based artificial neural network (ANN) to identify structural damage and nonlinear hysteresis parameters simultaneously is presented. To avoid the ‘dimensional disaster’, the principal component analysis technique is applied to eliminate the redundant dimensionality of the acceleration data. The acquired principal components, which covers over 95% of the variability in data, are then employed as the input to the ANN, while the system parameters to be identified are defined as the output. ANN is an effective tool to tackle complex problems in numerous fields. However, if using the gradient-descend algorithm to train the ANN model, with vanishing or exploding gradients, ANN may suffer the local minimal during the training process. To address this drawback, a new evolutionary algorithm, termed the K-means Jaya, is employed to train the ANN model to obtain optimal weights and biases by minimizing the discrepancies between real outputs and desired ones. The optimal weights and biases are then used to configure the ANN. The proposed method is applied to single and multiple degree-of-freedom nonlinear systems subjected to different external loadings. Results demonstrate the structural damage and nonlinear hysteresis parameters can be accurately identified.

Identification of Structural Reliability Parameters in Distributed Telecommunication System

The article considers estimation of structural parameters for communication network. The article aims to calculate the key indicators of structural reliability. Proposed method consists of mathematical expectation estimation for number of connections and connectivity probability in different structured networks. The methodology is based on solution to the problem of network optimization structure for reliability view. Classification of equipment is proposed in terms of failure impact on quality management of network traffic. According to the accepted classification, reliability parameters are listed characterizing each of the classified equipment groups. Reliability parameters are divided into single and complex ones. The reliability and quality of the communication network functioning is assessed by the estimated values of these parameters. A probabilistic graph of network model and state diagram of communication lines for one hierarchical level were plotted. When plotting, it was assumed that switching nodes are absolutely reliable. The reliability of communication lines is expressed in terms of availability. The calculation of parameters was carried out on the assumption that communication line failures are statistically independent events. Reliability indicators are estimated, such as the probability of connectivity between each pair of telecommunication nodes and the absolute value of mathematical expectation for number of connections. For convenience, practical application of the proposed method, calculation results of the mathematical expectation of number of connections is expressed as a percentage. The article considers a mathematical model of the functioning of a separate communication line with the absolute reliability of the system for monitoring its technical condition. The mathematical model is presented in the form of a state diagram. It is assumed that the network monitoring system performs the functions of periodic and continuous monitoring. A method for calculating complex indicators of reliability, such as the availability factor and the downtime factor, is proposed. The calculation method is based on the matrix method for analyzing probabilistic systems. The calculation formulas include statistical data from the system for monitoring the technical condition of communication lines. Modelling is used to simulate the process of communication network functioning in experimental research. The process of communication network functioning is simulated. Depending on the incoming load, a search for free algorithmic resources and communication lines is performed. The calculation of the reliability indicators of the selected way of servicing calls is performed, taking into account the selection criterion set by the program. The monitor displays a dependency graph of the reliability indicators on the intensity of the incoming load. The calculation of the relative statistical frequency of the success of servicing requests is carried out, and the frequency of communication lines failure is also calculated. The program output is proposals for communication network reconstruction due to network resources redistribution.

Instrumental variable estimation of the marginal structural Cox model for time-varying treatments.

Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of marginal structural model parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. The marginal structural Cox model is one of the most popular marginal structural models for evaluating the causal effect of time-varying treatments on a censored failure time outcome. In this paper, we establish sufficient conditions for identification of marginal structural Cox model parameters with the aid of a time-varying instrumental variable, in the case where sequential randomization fails to hold due to unmeasured confounding. Our instrumental variable identification condition rules out any interaction between an unmeasured confounder and the instrumental variable in its additive effects on the treatment process, the longitudinal generalization of the identifying condition of Wang & Tchetgen Tchetgen (2018). We describe a large class of weighted estimating equations that give rise to consistent and asymptotically normal estimators of the marginal structural Cox model, thereby extending the standard inverse probability of treatment weighted estimation of marginal structural models to the instrumental variable setting. Our approach is illustrated via extensive simulation studies and an application to estimating the effect of community antiretroviral therapy coverage on HIV incidence.

Modal Parameter Identification of Structures Using Reconstructed Displacements and Stochastic Subspace Identification

A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the model order. However, due to the difficulty of displacement measurement, different types of noise interferences occurred when an acceleration sensor was used, causing a trend term drift error in the integral displacement. A moving average low-frequency attenuation frequency-domain integral was used to reconstruct displacements, and the moving time window was used in combination with the SSI method to identify the structural modal parameters. First, measured accelerations were used to estimate displacements. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. The moving average method was then used in combination with a filter to effectively eliminate the random fluctuation interference in measurement data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, using reconstructed displacements as inputs, the improved SSI method was employed to identify the modal parameters of the structure.

Sensor placement for model identification of multi-story buildings under unknown earthquake ground motion

The number of sensors and their position is very important for the acquisition of high-quality measurement data in structural health monitoring (SHM), where the detection and evaluation of structural health status under unknown seismic ground motion have been an important research area. It is undoubtedly that the identification of structural model parameters under unknown seismic ground motion or joint identification of model parameters and seismic time-history input depends on the effective placement of sensors on structures. At present, the investigation of sensor configuration is mainly intended for structural model or modal parameter identification without taking into account the seismic effect, which cannot guarantee the effective contribution of the dynamic measurement data to the joint identification of ground motion and model parameters. To the best of authors knowledge, the optimal sensor configuration for the purpose of model parameter identification under unknown seismic ground motion has not yet been reported in the current literature. In the framework of Bayes'theorem and information theory, this paper proposes an optimal sensor placement methodology targeting at model parameter identification under unknown seismic ground motion. Based on the joint identification of ground motion and structural model parameters, the optimal configuration scheme for a given number of sensors is determined by maximizing a proposed expected entropy measure formed by utilizing the marginal distribution of model parameters prior to the availability of the measurement data. In addition, to solve the combinatorial optimization problem arising from the optimal sensor configuration, this paper also proposes a simple yet efficient strategy for revising the fitness function of genetic algorithm, which converts the continuous variable representation of the sensor configuration vector into a binary string representation. This completely avoids the common approaches relying on modifying various genetic operators to ensure the mandatory constraint of always configuring a fixed number of sensors. The validity of the proposed methodology is fully validated by numerical and experimental case studies conducted respectively for a 20-story shear-building model and a laboratory 6-story shear-building model under shaking table test.

Structural-parametric identification of the sum of hyperbolic functions with additive noise

The paper proposes a two-step method for structural-parametric identification of hyperbolic functions with additive noise. At the first stage, a linear difference equation is associated with the sum of hyperbolic functions. For the estimation of the parameters of the difference equation, the method of generalized total least squares (GTLS). To solve the GTLS problem, an augmented system equivalent to a biased normal system is applied. In the second stage, the missing parameters are estimated. The simulation results showed that the use of GTLS in the first stage improves the accuracy of the estimate compared to Ordinary Least Squares (OLS).

Identification of time-varying nonlinear structural physical parameters by integrated WMA and UKF/UKF-UI

Identification of time-varying nonlinear structural physical parameters by integrated WMA and UKF/UKF-UI

Parameter identification of nonlinear bistable piezoelectric structures by two-stage subspace method

Parameter identification of nonlinear bistable piezoelectric structures by two-stage subspace method

Identification of modal parameters from non-stationary responses of high-rise buildings

A key issue in the control, health monitoring, and condition assessment of civil structures is the estimation of structural modal parameters based on measured structural responses. However, field measurements of structural responses from civil structures under strong wind or earthquake excitations usually exhibit non-stationary feature and therefore cannot be adequately deal with by traditional modal identification methods. In this study, a novel procedure is integrated for modal parameter identification of civil structures from non-stationary structural responses on the basis of the variational mode decomposition (VMD) technique. First, the VMD algorithm is applied to decompose measured vibration signals into individual mode components. Then, the random decrement technique (RDT) is employed to obtain free vibration response of each mono component. Next, normalized Hilbert transform (NHT) is used to estimate modal natural frequency and damping ratio. The performance of the developed approach is evaluated using simulated non-stationary responses of a frame structure, and the identified results are validated. The effects of crucial factors such as levels of noise involved in structural response and data length on the modal parameter estimations are examined through detailed parametric study. Furthermore, the approach is applied to modal identification based on field measured non-stationary responses of a high-rise building during Typhoon Nida. The case study illustrates that the integrated method is an efficient tool for estimating the modal parameters of civil structures from non-stationary structural responses.

Bayesian model updating of civil structures with likelihood-free inference approach and response reconstruction technique

Bayesian inference methods typically require a considerable amount of computation time in the calculation of forward models. This limitation restricts the application of Bayesian inference methods for the parameter identification of complex engineering problems. We propose a novel likelihood-free Bayesian inference method for structural parameter identification. An adaptive Gaussian surrogate model (GSM) was integrated with the transitional Markov chain Monte Carlo (MCMC) method for Bayesian inference. The log-likelihood function was approximated with GSM and the transitional MCMC method was used to generate the posterior distribution samples. A response reconstruction technique was combined with the likelihood-free Bayesian inference method for the parameter identification. Both numerical studies and experimental studies were conducted to verify the accuracy and efficiency of the proposed method. The results showed that the proposed method could be used to estimate the posterior probabilities of unknown structural parameters. Additionally, the proposed method was more efficient than the delayed rejection adaptive Metropolis and Gibbs sampling methods.

Identification of a mathematical model for a single-shaft power-generating GTE

The design and development processes of gas turbine engines rely on the usage of mathematical models representing the physics of engine functioning processes. One way of increasing the validity of a mathematical model is its identification based on engine test results. The identification of mathematical models of modern power-generating gas turbine engines (GTEs) presents a demanding and time-consuming task due to the necessity to identify the main controlled engine parameters determined in the course of experimental studies depending on a large number of the parameters that are not controlled during the experiment. In this regard the actual direction of reducing the labour intensity of the process of mathematical model identification is using identification program complexes. The object of the study was to solve the problem of structural-parametrical identification of the power-generating GTE functioning model detailing the turbine flow path calculations to the level of blade rows in order to obtain the GTE mathematical model that describes the characteristics of a real engine with given accuracy. To achieve the objective, the following problems were solved: variable parameters, controlled parameters and characteristics, ranges of their variations were selected from the total number of the mathematical model input data, the objective functions were defined; the task of the parametric identification according to the results of bench tests through GTE operating modes was performed; analytical approximating dependences for correcting coefficients (variable parameters) were obtained; structural-parametric identification of the mathematical model was performed. The novelty of the obtained results is the identification of the mathematical model of the nonlinear component GTE of the second level performed without model linearization (without its level lowering) by using the Optimum software packages. The methodological approach for the parametric identification of the mathematical model is proposed. This approach allows reducing the number of variable parameters under the modes lower that the maximum. It shows that the identified model allows obtaining the prediction results of the GTE parameters and characteristics through operating modes with a deviation of no more than 1.4% from the experimental data and, therefore, it will allow reduction of terms and an increase in the quality of power unit development.

Проблеми системного аналізу космічної діяльності в Україні. Динаміка і керування рухом ракет та космічних апаратів

This paper overviews the main research results obtained from 2016 to 2020 at the Department of System Analysis and Control Problems of the Institute of Technical Mechanics of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine. The overview was prepared using the method of phenomenological analysis. The results of the system analysis of the trends in the use of space technologies in different areas of human activities are presented. Methods of draft space program evaluation and R&D program formation algorithms are discussed. Research results on the dynamics of spacecraft and components thereof are presented. The dynamics of tethered space systems and microsatellites is considered. Consideration is given to the world’s trends in the development of space transportation systems, the features of their development in Ukraine, and the state of the art of the methodological basis of rocket/space hardware design in Ukraine. Recommendations on working out normative documents and lines of their refinement are given. System analysis methods were considered and found their further development as applied to the space sector. A method of structural-parametric identification was developed for the problem of object simulation with a multidimensional output in the class of beta-autoregressive models, in which autoregrassion weight coefficient ratios are determined based on beta-distribution density functions. On principles of the group data handling method, a regularity criterion was constructed and studied for simulation in the class of beta-autoregressive models in conditions of quasi-repeated observations. Investigations were conducted into the analysis, synthesis, and operation optimization of systems of multilevel, intellectual, and network management under uncertainty. Methodological approaches to the development of symbiotic self-contained aerospace and transport systems were elaborated. A new technology was developed for the construction of surrogate models with the use of approaches on the basis of the synergy of subject field methods and cognitive technologies based on general scientific achievements. Part of the research results has found prectuical use at Yuzhnoye State Design Office.

A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures

The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropriate initial target parameters give the unscented Kalman filter a head start, the uncertainty levels and noise parameters set the rate of convergence in the process. Therefore, due to the coupling effect of these parameters, an inclusive approach is desired to maintain the chance of convergence for expensive experimental tests. In this paper, a framework is proposed that, via a virtual emulation prior to the experiment, determines a set of initial conditions to ensure a successful application of the online parameter identification. A Bayesian optimization method is proposed, which considers the level of confidence in the initial guesses for the target parameters to suggest the appropriate noise covariance matrices. The methodology is validated on a five-story shear frame tested on a shake table. The results indicate that, indeed, a trade-off can be made between the robustness of the online updating and the final parameter accuracy.