Prof. Jian Zhang Profile

Prof. Jian Zhang

at Southeast Univ

SPIE Involvement:

Author

Proceedings Article | 5 April 2012 Paper

Structural flexibility identification by integrating substructures' measurements

Jian Zhang, F. Moon

Proceedings Volume 8347, 834708 (2012) https://doi.org/10.1117/12.914627

KEYWORDS: Bridges, Matrices, Signal processing, Sensors, Testing and analysis, Structural health monitoring, Safety, Data acquisition, Data processing, Systems engineering

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Proceedings Article | 20 April 2011 Paper

Rapid impact testing for quantitative assessment of large populations of bridges

Yun Zhou, John Prader, John DeVitis, Adrienne Deal, Jian Zhang, Franklin Moon, A. Emin Aktan

Proceedings Volume 7983, 798322 (2011) https://doi.org/10.1117/12.880931

KEYWORDS: Bridges, Sensors, Modal analysis, Structural health monitoring, Calibration, Inspection, Finite element methods, Data modeling, Reliability, Fourier transforms

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Proceedings Article | 30 March 2009 Paper

Ambient data analysis for robust and efficient structural identification

Jian Zhang, Franklin Moon, Ahmet Aktan

Proceedings Volume 7292, 729237 (2009) https://doi.org/10.1117/12.816123

KEYWORDS: Autoregressive models, Data modeling, Data analysis, Filtering (signal processing), System identification, Bridges, Process modeling, Structural health monitoring, Signal processing, Reliability

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Proceedings Article | 11 April 2007 Paper

Advanced signal processing for structural identification: experimental studies

Jian Zhang, Tadanobu Sato, Tara Hutchinson

Proceedings Volume 6532, 65320S (2007) https://doi.org/10.1117/12.715109

KEYWORDS: Earthquakes, Signal processing, Structural health monitoring, Data processing, Filtering (signal processing), Neural networks, Motion models, Bismuth, System identification, Structural engineering

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Proceedings Article | 11 April 2006 Paper

Linear and nonlinear structural identifications using the support vector regression

Jian Zhang, Tadanobu Sato

Proceedings Volume 6174, 61743D (2006) https://doi.org/10.1117/12.658419

KEYWORDS: Structural health monitoring, Data modeling, System identification, Neural networks, Sensors, Bismuth, Motion models, Control systems, Interference (communication), Statistical analysis

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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.

Proceedings Article | 7 February 2006 Paper

Study of lag effect in LPHD plasma etching of Si for MEMS applications by variable time steps in numerical methods

Jian Zhang, Qing-An Huang, Wei-Hua Li

Proceedings Volume 6032, 60320H (2006) https://doi.org/10.1117/12.667866

KEYWORDS: Etching, Plasma etching, Ions, Chlorine, Silicon, Computer simulations, Microelectromechanical systems, Numerical simulations, Plasma, Numerical analysis

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Proceedings Article | 7 February 2006 Paper

An ICP etch model based on time multiplexed deep etching

Jian Zhang, Qing-An Huang, Wei-Hua Li

Proceedings Volume 6032, 60320G (2006) https://doi.org/10.1117/12.667865

KEYWORDS: Etching, Ions, 3D modeling, Multiplexing, Silicon, Plasma, Microelectromechanical systems, Computer simulations, Polymers, Deep reactive ion etching

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Proceedings Article | 17 May 2005 Paper

Support vector regression for structural identification

Jian Zhang, Tadanobu Sato

Proceedings Volume 5765, (2005) https://doi.org/10.1117/12.598384

KEYWORDS: Systems modeling, Data modeling, Structural dynamics, Neural networks, Error analysis, System identification, Machine learning, Motion models, Mathematical modeling, Autoregressive models

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Showing 5 of 8 publications

Conference Committee Involvement (2)

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

8 March 2010 | San Diego, California, United States

Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security IV

8 March 2010 | San Diego, California, United States

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