Substructural Identification Method without Interface Measurement

Substructural identification provides a novel means by which to reduce a large problem to smaller problems of manageable size, thereby improving numerical convergence and accuracy. Various methods proposed by several researchers thus far require interface response measurements, which are then treated as input to the substructures of concern. In practice, however, it is not always possible to obtain interface measurements, particularly if rotational response is required for beam/frame structures. In this paper, a method for parameter identification of substructures without the need of interface measurements is proposed. On the basis of receptance theory, an inverse problem is formulated in the frequency domain. Interface forces are eliminated by using different sets of measurements in the substructure concerned under the same dynamic excitation. The genetic algorithms approach is employed to determine the unknown parameters, and the fitness function is defined to minimize the difference between the estimates of interface forces obtained using different sets of response measurements. Three numerical examples are presented to illustrate the proposed method, and account for effects of measurement noise.     

Study of Time-Domain Techniques for Modal Parameter Identification of a Long Suspension Bridge with Dense Sensor Arrays

While numerous studies have been published concerning the application of a variety of system identification techniques in conjunction with vibration measurements from civil infrastructure systems, there is a paucity of publications addressing the influence of algorithm-specific control parameters that impact the correct and efficient application of the selected identification scheme. Furthermore, as dense sensor arrays become widely accessible in civil infrastructure applications, voluminous amounts of multichannel data streams are becoming available for processing, thus imposing new demands on identification procedures regarding high-dimensionality (in both the spatial as well as the temporal domains) requirements that may render some methods inapplicable if careful attention is not paid to practical implementation issues. This paper provides a comprehensive study of three time-domain identification algorithms applied in conjunction with the Natural Excitation Technique in order to extract the modal parameters of a newly constructed long-span bridge that was monitored, in its virgin state, over a relatively long period of time with a state-of-the-art dense sensor array. The three methods used are: the eigensystem realization algorithm (ERA), the ERA with data correlations, and the least squares algorithm. One of the critical issues in the mentioned algorithms, is selection of the reference degree-of-freedom (DOF). Previous experiences have shown that one cannot rely on a single reference DOF for identification of all modes. Consequently, the aforementioned identification formulations were modified to include multiple reference DOF, simultaneously, or one at a time. An autonomous algorithm was presented to distinguish the genuine structural modes from spurious noise or computational modes. Based on some parameter studies, some useful guidelines for the selection of critical user-selectable parameters are presented.     

Time Domain Identification of Frames under Earthquake Loadings

The purpose of this paper is to examine the adaptive identification method and the nonlinear system identification technique through numerical simulation on seismic response of building structures. For the identification of a time-variant system and a system with an abrupt change of modal parameters, two parameter adaptation algorithms in recursive identification methods are discussed: recursive least square algorithm with constant trace and adaptive fading Kalman filter method. Based on the equivalent linear model, the time-variant model parameters can be identified. The tracking ability on the identification of a time-variant system is discussed. For the identification of the nonlinear system, the forward including algorithm and the neural network training algorithm were used to estimate the modal parameter of the nonlinear autoregressive moving average model, and the accuracy on the predicted output of nonlinear model identification was discussed.     

Numerical Approximations of Design Points in Reliability Analysis under Parametric Changes

Traditional approaches for repeatedly updating reliability estimates, as needed in reliability-based optimal designs or real-time system control, require the iterative application of a reliability method. This paper explores a new strategy for repeatedly estimating reliability under frequent parameter variations. The central idea is to update the design point in the parameter domain, rather than in the traditional random variable domain, by evaluating several parametric sensitivity measures which are systems of nonlinear first-order ordinary differential equations relating the design point to parameter changes. Four numerical algorithms for evaluating the sensitivity measures are developed using the Euler and the improved Euler algorithms. Two solution procedures are applied. One procedure solves for the updated design point directly, while the other solves for both the unit normal vector at the design point and the reliability index separately, and evaluates the product of these to determine the updated design point. The numerical techniques are thoroughly compared with the classical Hasofer and Lind-Rackwitz and Fiessler (HL-RF) algorithm in five numerical examples regarding efficiency and accuracy. It is found that they are efficient and robust under given conditions.     

Assessing Parameter Identifiability of Activated Sludge Model Number 1

The main difficulties reported in the identification of biokinetic models describing the activated sludge process are related to poor convergence or nonconvergence of the identification algorithms and nonuniqueness of the parameter estimates (i.e., different values for the parameters produce approximately the same response from the model). In the present paper, we assessed the identifiability of the Activated Sludge Model Number 1 parameters for a simulated full-scale WWTP calibrating situation, using both noise-free and noise-corrupted simulated data, and analyzed the efficiency of different optimization methods in the identification process. We began by comparing the performance, in terms of the rate of convergence, for different identification algorithms based on three distinct optimization methods. Finally, a procedure based on the information content of the Fisher and covariance matrices was applied in order to define the set of best identifiable parameters in different calibration situations.     

Parameter Estimation for Multiple-Input Multiple-Output Modal Analysis of Large Structures

Experimental modal analysis (EMA) has been explored as a technology for condition assessment and damage identification of constructed structures. However, successful EMA applications such as damage detection to constructed systems pose certain difficulties. The properties of constructed systems are influenced by temperature changes as well as other natural influences such as movements in addition to any deterioration and damage. Writers were challenged in their attempts to measure the dynamic properties of an aged bridge by EMA due to inconsistencies within the data set due to short-term variations in ambient conditions. A complex interaction was observed between the dynamic properties of the bridge, hour-to-hour changes in temperature, and controlled damages applied to the bridge. Inconsistencies in the data set made curve fitting difficult for some common parameter estimation algorithms that have been designed to handle consistent data sets. Although the quality of measurements within the entire data set was affected by time variance and nonlinearity, increasing the number of reference measurements significantly improved the reliability of the information which could be extracted. In conjunction with the multiple-input multiple-output technique, a parameter estimation method using complex mode indicator function (CMIF) was developed and implemented in this study to determine the modal properties with proper scaling to obtain modal flexibility. This method proved to be very successful among many others with the data acquired from the aged and deteriorated highway bridge. In this paper, challenges in reliable identification of modal parameters from large structures are reviewed and the new CMIF based algorithm is documented. The method is evaluated on actual bridge data sets from a damage detection research study.     

Identification of Manning’s Roughness Coefficients in Shallow Water Flows

A numerical method based on optimal control theories for identifying Manning’s roughness coefficients (Manning’s n) in modeling of shallow water flows is presented. The coefficients are difficult to be determined especially when the spatial variation is significant, and are usually estimated empirically. The present methodology is applied to determine the optimal values of the spatially distributed parameters, which give least overall discrepancies between simulations and measurements. Through a series of systematic studies to identify the n values in both a hypothetical open channel and a natural stream stretch, several identification procedures based on unconstrained and constrained minimizations are analyzed. It is found that the limited-memory quasi-Newton method has the advantages of higher rate of convergence, numerical stability and computational efficiency. Although the identification of Manning’s n is chosen as an example, the identification methods can be applied to numerical simulations of various flow problems.     

Mixed Integer Nonlinear Least-Squares Problem for Damage Detection in Truss Structures

We present a mixed integer nonlinear least-squares problem for identifying damage in truss structures from their measured response. In detecting damage based on parameter estimation, the number of unknown parameters is often less than that of measurements, which gives rise to nonunique solutions. To overcome the difficulty, we formulate damage detection as a mixed integer nonlinear least-squares problem, where the subset of unknown parameters is sought that best represents damaged sites. To solve the problem, we present four heuristic algorithms based on the greedy algorithm. One is its direct application. The other three select the near-optimal subsets more efficiently by linearizing the error function, by applying the line search, and by grouping unknown parameters. We assess the performance of these algorithms along with conventional regularization methods through numerical experiments, where many synthetic damage cases are tested. The effect of modeling and measurement errors on the estimate is also studied. We found from the numerical experiments that the linearization-based approach was more efficient than the direct application while the two methods gave reasonably accurate estimates.     

机器人负载的动力学参数辨识

为解决机器人末端负载的时变性给高速运动的机器人带来控制精度降低的问题,研究了参数差值法、力矩求解法、全局参数辨识法的机器人末端负载动力学参数辨识的方法,以提高末端负载的辨识精度.得到的负载动力学参数用于动力学控制以提高机器人动态精度.通过建立拉格朗日动力学线性辨识模型,以最优激励轨迹进行实时数据采集,采样数据经过低通滤波及中心差分的处理后,代入相应的负载辨识方程式,并用加权最小二乘法解决线性方程组,可辨识到不同负载的动力学参数.实验验证了负载辨识方法的可行性.      

Closed Loop Predictive Optimal Control Algorithm Using ARMA Models

A predictive optimal control algorithm based on auto-regressive moving average (ARMA) models has been developed. Being a closed loop algorithm, it can be used in the control of structures against any source of random excitation without any special arrangement for measuring the excitation. The algorithm combines two mathematical models: (1) An ARMA model representing the environment and the structure as a single system subjected to the unknown random excitation; and (2) a single-degree-of-freedom system, which represents the structure subjected to a known history of control force only. Unlike some ARMA based control algorithms, which are only for seismic excitations, the proposed algorithm permits the control system to be operational under any type of dynamic load. This results in a large reduction in the standby time of the control system and increases its reliability. In comparison with these algorithms, the proposed algorithm requires less measurements and sensors. The algorithm has the option for reducing or avoiding time delay.     

Texture-Based Optimization of Crystal Plasticity Parameters: Application to Zinc and Its Alloy

Evolutionary algorithms have become an extensively used tool for identification of crystal plasticity parameters of hexagonal close packed metals and alloys. However, the fitness functions were usually built using the experimentally measured stress–strain curves. Here, the fitness function is built by means of numerical comparison of the simulated and experimental textures. Namely, the normalized texture difference index is minimized. The evolutionary algorithm with the newly developed fitness function is tested by performing crystal plasticity parameter optimization for both pure zinc and zinc-magnesium alloy. These materials are promising candidates for bioabsorbable implants due to good biocompatibility and optimal corrosion rate. Although their mechanical properties in the as-cast state do not fulfill the requirements, they can be increased by means of hydrostatic extrusion. The developed modeling approach enabled acquisition of the crystal plasticity parameters and analysis of the active deformation mechanisms in zinc and zinc-magnesium alloy subjected to hydrostatic extrusion. It was shown that although slip systems are the main deformation carrier, compressive twinning plays an important role in texture evolution. However, the texture is also partially affected by dynamic recrystallization which is not considered within the developed framework.

     

基于遗传算法优化的支持向量机品位插值模型

将支持向量机(SVM)和遗传算法(GA)集成应用到矿体品位插值问题中,利用遗传算法全局搜索的优势对支持向量机的三个关键参数——惩罚系数C、不敏感系数ε和核函数参数σ进行寻优,克服单纯支持向量机法中依靠经验确定参数的局限性.将优化参数代入到支持向量机中进行迭代训练,得到基于遗传算法参数优化的支持向量机(GA-SVM)矿体品位插值模型.以国内典型矿山的实际勘探数据为例,通过该品位插值模型计算结果与传统插值方法计算结果和矿山生产实际数据的对比分析,验证了其可行性和有效性.      

Genetic algorithms-based design and optimization of statistical quality-control procedures

In general, one cannot use algebraic or enumerative methods to optimize a quality-control (QC) procedure for detecting the total allowable analytical error with a stated probability with the minimum probability for false rejection. Genetic algorithms (GAs) offer an alternative, as they do not require knowledge of the objective function to be optimized and can search through large parameter spaces quickly. To explore the application of GAs in statistical QC, I developed two interactive computer programs based on the deterministic crowding genetic algorithm. Given an analytical process, the program "Optimize" optimizes a user-defined QC procedure, whereas the program "Design" designs a novel optimized QC procedure. The programs search through the parameter space and find the optimal or near-optimal solution. The possible solutions of the optimization problem are evaluated with computer simulation.     

Genetic-Algorithm-Based Strategies for Dynamic Identification of Nonlinear Systems with Noise-Corrupted Response

The main objective of this paper is to investigate efficiency and correctness of different real-coded genetic algorithms and identification criteria in nonlinear system identification within the framework of non-classical identification techniques. Two conventional genetic algorithms have been used, standard genetic algorithm and microgenetic algorithm. Moreover, an advanced multispecies genetic algorithm has been proposed: it combines an adaptive rebirth operator, a migration strategy, and a search space reduction technique. Initially, a critical analysis has been conducted on these soft computing strategies to provide some guidelines for similar engineering and physical applications. Therefore, the hysteretic Bouc-Wen model has been numerically investigated to achieve three main results. First, the computational effectiveness and accuracy of the proposed strategy are checked to show that the proposed optimizer outperforms the aforementioned conventional genetic algorithms. Secondarily, a comparative study is performed to show that an improved performance can be obtained by using the Hilbert transform-based acceleration envelope as objective function in the optimization problem (instead of the pure acceleration response). Finally, system identification is conducted by making use of the proposed optimizer to verify its substantial noise-insensitive property also in the presence of high noise-to-signal ratio.     

Constructing Second-Order Models of Mechanical Systems from Identified State Space Realizations. Part II: Numerical Investigations

This paper presents various numerical investigations about obtaining physical parameters of second-order mechanical systems using the algorithms that were investigated in detail in the first part of this study. To discuss in detail the computational aspects and the limitations of each of these algorithms, the first example presented is the identification of the physical parameters of a three-degrees-of-freedom (3-DOF) system. It is shown that when the input/output data used in the identification is noise free, then each of the methodologies can exactly retrieve the second-order coefficient matrices, provided that the sensor/actuator requirements imposed by each of them are satisfied accordingly. To investigate the effects of noisy data on the identified second-order parameters, Monte Carlo simulations are performed on the 3-DOF system at different noise-to-signal ratios, and the results show that the three algorithms perform quite satisfactorily. The analysis is then extended to a 120-DOF, three-dimensional structural system, and in this part of the presentation issues such as modal truncation, insufficient instrumentation, and alternative colocation requirements are discussed. The last section of the paper is devoted to a detailed evaluation of the performance of each of the algorithms discussed in this work.     

Identification of Structural Systems Using an Evolutionary Strategy

The problem of system identification is an inverse problem of difficult solution. Currently, difficulties lie in the development of algorithms that use measured data from the system to characterize it without significant a priori knowledge of the system. In this paper, a parameter estimation technique based on an evolution strategy (an optimization algorithm inspired by natural evolution) is presented to overcome some of the difficulties encountered in the field. Using this method, a set of direct problems is solved instead of directly tackling the inverse problem. If the uniqueness of the identification solution is guaranteed for the assumed model and the available data, this heuristic method is able to find a solution without incurring restrictions of other classical optimization methods, like the need for reliable initial estimates and convergence to local optima. Some results obtained with this algorithm are presented for the identification of 3 degrees of freedom (DOF) and a 10?DOF structural system under conditions including limited input/output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness of the system.     

PANSYM: a symbolic equation generator for mathematical modelling, analysis and control of metabolic and pharmacokinetic systems

Software is presented for automatic generation of first-order ordinary differential equations (ODE) that arise from lumped parameter representations of metabolic and pharmacokinetic systems. The definition of system structures is accomplished by fractional transfer rates between state variables, together with input/output equations and initial conditions of state variables. General non-linear mathematical expressions can be assigned to all structure definition items. The software parses and interprets the system definitions and generates symbolically the mathematical expression of the model's set of ODE. In addition, symbolic derivatives of state equations are determined with respect to model parameters, state variables and external inputs. These derivatives represent the constituents of systems of sensitivity-differential and adjoint-differential equations that arise in identification and optimal control problems. Finally, output routines generate source code that, once compiled and linked to simulation programs, allows efficient numerical integration of the system of ODE. This software has been developed in PROLOG on Macintosh computers and has been extensively used with the programming environment MATLAB. Possible applications of this software include model building, sensitivity analysis, identification, optimal experiment design and numerical solution of optimal control problems.     

混合选别过程半实物仿真系统

为了满足复杂工业过程控制技术的研究需求,需要建立具有代表性的半实物仿真系统.针对混合选别过程,研发由对象计算机、控制器设计计算机、监控计算机、虚拟执行机构与检测仪表装置和控制系统组成的半实物仿真系统.该系统基于工业控制系统软件开发控制算法,运用MATLAB研发虚拟对象、虚拟执行机构和检测仪表、控制器设计模型,研发了相应的可视化界面.在对象计算机、控制器设计计算机和监控计算机的基础上完成了被控对象机理建模、控制器设计模型参数辨识、控制器设计和控制器性能评价等研究.为复杂控制算法研究进一步工业应用奠定了基础.      

Optimal Design with Probabilistic Objective and Constraints

Significant challenges are associated with solving optimal structural design problems involving the failure probability in the objective and constraint functions. In this paper, we develop gradient-based optimization algorithms for estimating the solution of three classes of such problems in the case of continuous design variables. Our approach is based on a sequence of approximating design problems, which is constructed and then solved by a semiinfinite optimization algorithm. The construction consists of two steps: First, the failure probability terms in the objective function are replaced by auxiliary variables resulting in a simplified objective function. The auxiliary variables are determined automatically by the optimization algorithm. Second, the failure probability constraints are replaced by a parametrized first-order approximation. The parameter values are determined in an adaptive manner based on separate estimations of the failure probability. Any computational reliability method, including first-order reliability and second-order reliability methods and Monte Carlo simulation, can be used for this purpose. After repeatedly solving the approximating problem, an approximate solution of the original design problem is found, which satisfies the failure probability constraints at a precision level corresponding to the selected reliability method. The approach is illustrated by a series of examples involving optimal design and maintenance planning of a reinforced concrete bridge girder.