Modal parameter identification of a multiple-span post-tensioned concrete bridge using hybrid vibration testing data

The Alfred Zampa Memorial Bridge (AZMB), a newly built long-span suspension bridge, is located 32km northeast of San Francisco on interstate Highway I-80. A set of dynamic field tests were conducted on the AZMB in November 2003, just before the bridge opening to traffic. These tests provided a unique opportunity to identify the modal properties of the bridge in its as-built condition with no previous traffic loads or seismic excitation. A benchmark study on modal identification of the AZMB is performed using three different state-of-the-art system identification algorithms based on ambient, as well as forced vibration measurements. These system identification methods consist of: (1) the multiple-reference natural excitation technique combined with the eigensystem realization algorithm; (2) the data-driven stochastic subspace identification method; and (3) the enhanced frequency domain decomposition method. Overall, the modal parameters identified using these system identification methods are found to be in very good agreement for each type of tests (ambient and forced vibration tests). For most vibration modes, the natural frequencies and mode shapes identified using the two different types of test data also match very well. However, the modal damping ratios identified from forced vibration test data are, in general, higher than those estimated from ambient vibration data. The identified natural frequencies and mode shapes are finally compared with their analytical counterparts from a three-dimensional finite-element model of the AZMB. The modal properties of the AZMB presented in this paper can be used as baseline in future health monitoring studies of this bridge.

An increasing number of researchers in psychology are collecting intensive longitudinal data in order to study psychological processes on an intraindividual level. An increasingly popular way to analyze these data is autoregressive time series modeling; either by modeling the repeated measures for a single individual using classic n = 1 autoregressive models, or by using multilevel extensions of these models, with the dynamics for each individual modeled at Level 1 and interindividual differences in these dynamics modeled at Level 2. However, while it is widely accepted in psychology that psychological measurements usually contain a certain amount of measurement error, the issue of measurement error is largely neglected in applied psychological (autoregressive) time series modeling: The regular autoregressive model incorporates innovations, or "dynamic errors," but not measurement error. In this article we discuss the concepts of reliability and measurement error in the context of dynamic (VAR(1)) models, and the consequences of disregarding measurement error variance in the data. For this purpose, we present a preliminary model that accounts for measurement error for constructs that are measured with a single indicator. We further discuss how this model could be used to investigate the between-person reliability of the measurements, as well as the (person-specific) within-person reliabilities and any individual differences in these reliabilities. We illustrate the consequences of assuming perfect reliability, the preliminary model, and reliabilities, using an empirical application in which we relate women's general positive affect to their positive affect concerning their romantic relationship. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The instrumented Canton Tower is a 610 m high-rise structure, which has been considered as a benchmark problem for structural health monitoring (SHM) research. In this paper, an improved automatic modal identification method is presented based on a natural excitation technique in conjunction with the eigensystem realization algorithm (NExT/ERA). In the proposed modal identification method, damping ratio, consistent mode indicator from observability matrices (CMI_O) and modal amplitude coherence (MAC) are used as criteria to distinguish the physically true modes from spurious modes. Enhanced frequency domain decomposition (EFDD), the data-driven stochastic subspace identification method (SSI-DATA) and the proposed method are respectively applied to extract the modal parameters of the Canton Tower under different environmental conditions. Results of modal parameter identification based on output-only measurements are presented and discussed. User-selected parameters used in those methods are suggested and discussed. Furthermore, the effect of environmental conditions on the dynamic characteristics of Canton tower is investigated.

The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs), the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR) time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs) were then established and were trained using the Levenberg–Marquardt algorithm (L–M algorithm). The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.

Vector Autoregressive Modal Analysis With Application To Ship Structures

About the Y/C and noise of digital television, this paper based on the DSP adaptive filter is applied for system identification and noise cancellation in the Y/C processing system. It expounds the principle of adaptive FIR filter based on LMS algorithm and application in system identification and the realization method on fixed-point DSP.

Measurement Errors in System Identification and the Practical Universality of Low-Order LS and AR Models

Gamma Autoregressive Models and Application on Kizilirmak Basin Stochastic models are required to generate synthetic values of flows statistically similar to observed ones to use in the simulation studies of water related structures. Such models are extremely important for generation of flows of streams with short record length. Autoregressive time series (AR) models are the most used models in this area. In recent years, AR models valid for gamma distribution are developed. In these studies, method of moments and modified maximum likelihood methods are used to estimate the model parameters. The aim of this study are comparing two AR models under gamma distribution assumption and applying these models for monthly data set of EIE 1501 and EIE 1517 streamflow gauging stations located in Kizilirmak Basin. The results of each model are interpreted for researchers using these models in their further studies.

A new time series model for exponential variables having first-order autoregressive structure is presented. Unlike the recently studied standard autoregressive model in exponential variables (ear(1)), runs of constantly scaled values are avoidable, and the two parameter structure allows some adjustment of directional effects in sample path behaviour. The model is further developed by the use of cross-coupling and antithetic ideas to allow negative dependency. Joint distributions and autocorrelations are investigated. A transformed version of the model has a uniform marginal distribution and its correlation and regression structures are also obtained. Estimation aspects of the models are briefly considered.

Experimental modal analysis of a single-link flexible robotic manipulator with curved geometry using applied system identification methods

Aircraft system identification has a five-decades-long tradition at German Aerospace Center (DLR). Over the last two decades, the research covered various topics related to system identification of fixed- and rotary-wing aircraft, nonconventional applications and atmospheric effects, the development of new flight-test procedures for system identification purposes, and specific aircraft model enhancements and corresponding parameter estimation. Comprehensive tools were developed that support this research and can be applied to a variety of different problems and types of vehicles. The paper starts with a short description of the different system identification methods used at DLR and the corresponding tools. The discussion of flight-test procedures and maneuver design as well as sensor fusion and flight-path reconstruction provides information on how to optimize the flight tests for system identification and to arrive at a consistent flight-test database. The examples for fixed-wing aircraft provide information on identification including abnormal conditions such as icing and interaction with atmospheric disturbances as well as modeling of structural mechanics and loads. The identification of high-order rotorcraft models that account for rotor and engine dynamics and even structural modes is discussed, and the identification of rotor mast moments as well as the identification of non-physics-based models and their integration into physics-based models are also covered. A final section shows that system identification can also be used to derive models for gyroplanes and parachutes as well as to derive control equivalent turbulence input models and to estimate complex wind field geometries. Thus, a broad overview of possible applications of system identification is given.

Hybrid artificial intelligence-time series models for monthly streamflow modeling

This paper exploits a known but insufficiently attended to idea for the autogressive modeling of multivariate stationary and nonstationary covariance time series. (The term multichannel is common in the engineering literature. Here, we use both terminologies, multivariate and muItich annel.) The idea is to model multichannel thing s one channel ar-a-time as an instantaneous response-orthogonal innovations autoregressive model. Such modeling can be realized using only scalar computations. That idea was implicit in Pagano (1978) and was explicit in a program MULMAR by Kitagawa (Akaike et al, 1979 ), in Newton (1982) and also Sakai (1982). Each of those papers only treated multichannel stationary time series AR modeling. Here , we exploit that idea to address time series analysis problems via the realization of autoregressive models of botti multi channel stationary and multi channel nonstationary covariance times series data.

COMPARISON STUDY OF SUBSPACE IDENTIFICATION METHODS APPLIED TO FLEXIBLE STRUCTURES

The research is dedicated in the study to time series and the ability of using fuzzy logic with it in order to develop forecasting approaches. In this research ,the time series (Autoregressive model) are linked with fuzzy logic in order to get on the Parameters of fuzzy time series models (fuzzy logic Autoregressive model),and applied that on the data of daily mistakes rates in charges production. The fuzzy Autoregressive model of time series gave forecasting more sutiable than those given by Autoregressive model.