Traditional multi-reference impact testing (MRIT) has the merit to identify not only the structural modal parameters but
also structural flexibility, however, it requires a large number of sensors mounted on the entire structure which leads to
expensive experiment cost. A new mobile impact testing method is proposed in this article for a more efficient flexibility
identification of bridges. In the proposed method, the structure under investigation is subdivided into smaller substructures
which are tested independently. Then the experimental data collected from all sub-structures are integrated by
taking the interface measurement as a reference for flexibility identification of the entire structure. The new impact
testing method only requires limited instrumentation, thus it can be performed rapidly and efficiently. Especially, the
signal processing procedure developed in the proposed method is able to identify the full flexibility matrix of the entire
structure from the sparse FRF matrices of the sub-structures. Numerical and experimental examples studied successfully
verify the effectiveness of the proposed method by comparing its results with those from the traditional MRIT method
for structural flexibility identification and deflection prediction.
Although the widely acknowledged shortcomings of visual inspection have fueled significant advances in the areas of
non-destructive evaluation and structural health monitoring (SHM) over the last several decades, the actual practice of
bridge assessment has remained largely unchanged. The authors believe the lack of adoption, especially of SHM
technologies, is related to the 'single structure' scenarios that drive most research. To overcome this, the authors have
developed a concept for a rapid single-input, multiple-output (SIMO) impact testing device that will be capable of
capturing modal parameters and estimating flexibility/deflection basins of common highway bridges during routine
inspections. The device is composed of a trailer-mounted impact source (capable of delivering a 50 kip impact) and
retractable sensor arms, and will be controlled by an automated data acquisition, processing and modal parameter
estimation software. The research presented in this paper covers (a) the theoretical basis for SISO, SIMO and MIMO
impact testing to estimate flexibility, (b) proof of concept numerical studies using a finite element model, and (c) a pilot
implementation on an operating highway bridge. Results indicate that the proposed approach can estimate modal
flexibility within a few percent of static flexibility; however, the estimated modal flexibility matrix is only reliable for
the substructures associated with the various SIMO tests. To overcome this shortcoming, a modal 'stitching' approach
for substructure integration to estimate the full Eigen vector matrix is developed, and preliminary results of these
methods are also presented.
Proc. SPIE. 7292, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2009
KEYWORDS: Data modeling, Reliability, Signal processing, Structural health monitoring, System identification, Bridges, Autoregressive models, Filtering (signal processing), Process modeling, Data analysis
Various uncertainties involved in the structural modeling and experiment processes greatly limit the application of the
system identification (St-Id) technology on the real-life structural health monitoring and risk-based decision making. An
efficient St-Id method is proposed to accurately identify structural modal parameters by using ambient test data with
various uncertainties. The random decrement technique is first applied to reduce random errors by averaging the test
data. Subsequently, a high order Vector Backward Auto-Regressive (VBAR) model is proposed to identify structural
modal parameters. The merit of the VBAR model is that it awards a determine way to separate the system modes
consisting of structural parameters and the extraneous modes arising due to uncertainties. The ambient vibration data
from a cantilever beam experiment is employed to demonstrate the effectiveness of the proposed St-Id method.
The aim of this study is to use observed data from a shaking table test to verify experimentally an SVR-based (support
vector regression) structural identification approach. The method has been developed in previous work and showed
excellent performance for large-scale structural health monitoring in numerical simulations. SVR is a promising data
processing method employing a novel
&egr;-insensitive loss function and the 'Max-Margin' idea. Thus the SVR-based
approach identifies structural parameters accurately and robustly. In this method, a sub-structure technique is used thus
the SVR-based analysis is reduced in dimension. Experimental validation is necessary to verify the method's capability
to identify structural status from real data. For this purpose, a five-floor shear-building shaking table test has been
conducted and two cases, input excitations to the shaking table of the Kobe (NS 1995) earthquake and a Sine wave with
constant frequency and amplitude are investigated.
Robust and efficient identification methods are necessary to study in the structural health monitoring field, especially when the I/O data are accompanied by high-level noise and the structure studied is a large-scale one. The Support vector Regression (SVR) is a promising nonlinear modeling method that has been found working very well in many fields, and has a powerful potential to be applied in system identifications. The SVR-based methods are provided in this article to make linear large-scale structural identification and nonlinear hysteretic structural identifications. The LS estimator is a cornerstone of statistics but less robust to outliers. Instead of the classical Gaussian loss function without regularization used in the LS method, a novel e-insensitive loss function is employed in the SVR. Meanwhile, the SVR adopts the 'max-margined' idea to search for an optimum hyper-plane separating the training data into two subsets by maximizing the margin between them. Therefore, the SVR-based structural identification approach is robust and accuracy even though the observation data involve different kinds and high-level noise. By means of the local strategy, the linear large-scale structural identification approach based on the SVR is first investigated. The novel SVR can identify structural parameters directly by writing structural observation equations in linear equations with respect to unknown structural parameters. Furthermore, the substrutural idea employed reduces the number of unknown parameters seriously to guarantee the SVR work in a low dimension and to focus the identification on a local arbitrary subsystem. It is crucial to make nonlinear structural identification also, because structures exhibit highly nonlinear characters under severe loads such as strong seismic excitations. The Bouc-Wen model is often utilized to describe structural nonlinear properties, the power parameter of the model however is often assumed as known even though it is unknown in the real world. In the case of unknown-power parameter, the nonlinear structural identification problem is more intricate and few approaches are dedicated to this problem. In this article, a model selection strategy is proposed to determine the unknown power parameter of the Bouc-Wen model. Meanwhile, the optimum SVR parameters are automatically selected instead of tuning manually. Based on the produced power parameter and optimum SVR parameters, the SVR is executed to identify nonlinear hysteretic structural parameters accurately and robustly. The numerical examples for two linear large-scale structures and a five-DOF nonlinear hysteretic structure provided illustrates that the proposed technique has excellent performance in robustness and accuracy for linear and nonlinear structural identifications, even when the noise exits in I/O measurements is high-level and non-Gaussion. Moreover, an incremental training algorithm utilized to solve SVR formulation in a sequential way not only significantly reduces the computation time, but also makes the structural health monitoring on-line.
A simplified numerical model for two-dimensional etched profile evolution is developed based on dynamics of plasma~surface interactions. The Lag effect, which is an important phenomenon in plasma etching, is detected in fixed-aspect-ratio structures by this model. So it may provide aid to theory and experiment research of plasma etching. The setting of time steps in numerical simulations is also discussed in this paper and the optimal time steps are proposed and verified.
A novel ICP etch model based on time multiplexed deep etching is reported in this paper. 2-D and 3-D zonal simulations of surface evolvement can be performed using this model. The simulation is advanced with the simplex algorithm for surface evolvement and consequent higher efficiency than other reported hybrid algorithms. And the etching of different material types can also be simulated using this model. Simulations with different aspect ratios are performed in this paper and the results are quite perfect without aforehand experimental fitting.
Structural identification based on the vibration data is still a challenging topic especially when the input and output (I/O) measurements are corrupted by high-level noise. In this paper, we propose a new structural parameter identification method based on the Support Vector Regression (SVR) which has been found working very well in many fields as an exclusively data based non-linear modeling method. Machine learning technologies such as Neural Networks has been applied widely in the field of health monitoring field. However, most papers just obtain the 'block-box' model of the studied structures from Neural Network training but the structural parameters are not identified actually. In our work, we not only generate the 'block-box' model but also identify the structural parameters by combining ARMA model together with SVR. Due to the “max-margin” idea used, SVR showed powerful properties in ARMA and structural identification under different kinds and amplitude noise. Furthermore, how to choose the parameters of SVR is also studied in this paper. Finally, numerical examples are given to demonstrate that the proposed method based on SVR is effective and powerful for identifying ARMA time series and structural models.