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CONTENTS
Volume 5, Number 1, January 2009
 

Abstract
A two-stage damage detection method is proposed and demonstrated for structural health monitoring. In the first stage, the subset selection method is applied for the identification of the multiple damage locations. In the second stage, the damage severities of the identified damaged elements are determined applying SSGA to solve the optimization problem. In this method, the sensitivities of residual force vectors with respect to damage parameters are employed for the subset selection process. This approach is particularly efficient in detecting multiple damage locations. The SEREP is applied as needed to expand the identified mode shapes while using a limited number of sensors. Uncertainties in the stiffness of the elements are also considered as a source of modeling errors to investigate their effects on the performance of the proposed method in detecting damage in real-life structures. Through a series of illustrative examples, the proposed two-stage damage detection method is demonstrated to be a reliable tool for identifying and quantifying multiple damage locations within diverse structural systems.

Key Words
damage detection; structural health monitoring; genetic algorithms; subset selection and model updating.

Address
Gun Jin Yun; Department of Civil Engineering, University of Akron, Akron, OH, USA
Kenneth A. Ogorzalek; Department of Civil Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
Shirley J. Dyke and Wei Song; Department of Mechanical, Aerospace and Structural Engineering, Washington University in St. Louis, St. Louis, MO, USA

Abstract
In order to reduce vibration or to control shape of structures made of metal or composites, piezoelectric materials have been extensively used since their discovery in 1880. A recent trend is also seen to apply piezoelectric materials to flexible structures made of rubber-like materials. In this paper a non-linear finite element model using updated Lagrangian (UL) approach has been developed for static analysis of rubber-elastic material with surface-bonded piezoelectric patches. A compressible stain energy function has been used for modeling the rubber as hyperelastic material. For formulation of the nonlinear finite element model a twenty-node brick element is used. Four degrees of freedom u, v and w and electrical potential j per node are considered as the field variables. PVDF (polyvinylidene fluoride) patches are applied as sensors/actuators or sensors and actuators. The present model has been applied to bimorph PVDF cantilever beam to validate the formulation. It is then applied to study the smart rubber components under different boundary and loading conditions. The results predicted by the present formulation are compared with the analytical solutions as well as the available published results. Some results are given as new ones as no published solutions available in the literatures to the best of the authors\' knowledge.

Key Words
nonlinear finite element; compressible strain energy function; hyperelastic material; piezo-rubber beam; smart rubber beam.

Address
M. C. Manna; Department of Applied Mechanics, Bengal Engineering and Science University, Howrah 711 103, India
A. H. Sheikh; School of Civil, Environmental and Mining Engineering, The University of Adelaide, South 5005, Australia
R. Bhattacharyya; Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur 721 302, India

Abstract
This study introduces a novel approach for locating damage in a structure using wireless sensor system with local level computational capability to alleviate data traffic load on the centralized computation. Smart wireless sensor systems, capable of iterative damage-searching, mimic an optimization process in a decentralized way. The proposed algorithm tries to detect damage in a structure by monitoring abnormal increases in strain measurements from a group of wireless sensors. Initially, this clustering technique provides a reasonably effective sensor placement within a structure. Sensor clustering also assigns a certain number of master sensors in each cluster so that they can constantly monitor the structural health of a structure. By adopting a voting system, a group of wireless sensors iteratively forages for a damage location as they can be activated as needed. Since all of the damage searching process occurs within a small group of wireless sensors, no global control or data traffic to a central system is required. Numerical simulation demonstrates that the newly developed searching algorithm implemented on wireless sensors successfully localizes stiffness damage in a plate through the local level reconfigurable function of smart sensors.

Key Words
damage detection; wireless sensor network; voting system; clustering technique.

Address
Min-Joong Jeong; Supercomputing Application Team, Korea Institute of Science and Technology Information, Daejeon, Korea
Bong-Hwan Koh; Department of Mechanical Engineering, Dongguk University, 3-26 Pil-dong, Chung-gu, Seoul 100-715, Korea

Abstract
In the present study, active control of a smart beam under forced vibration is analyzed. The aluminum smart beam is composed of two piezoelectric patches and strain gauge. One of the piezoelectric patches is used as controlling actuator while the other piezoelectric patch is used as vibration generating shaker. The smart beam is harmonically excited by the piezoelectric shaker at its fundamental frequency. The strain gauge is utilized to sense the vibration level. Active vibration reduction under harmonic excitation is achieved using both strain and displacement feedback control. Control actions, the finite element (FE) modeling and analyses are directly carried out by using ANSYS parametric design language (APDL). Experimental applications are performed with LabVIEW. Dynamic behavior at the tip of the beam is evaluated for the uncontrolled and controlled responses. The simulation and experimental results are compared. Good agreement is observed between simulation and experimental results under harmonic excitation.

Key Words
active control of forced vibration; piezoelectric; finite element analysis.

Address
L. Malgaca and H. Karagulle; Department of Mechanical Engineering, Dokuz Eylul University, 35100, Bornova / Izmir, Turkey

Abstract
A non-clipped semi-active stochastic optimal control strategy for nonlinear structural systems with MR dampers is developed based on the stochastic averaging method and stochastic dynamical programming principle. A nonlinear stochastic control structure is first modeled as a semi-actively controlled, stochastically excited and dissipated Hamiltonian system. The control force of an MR damper is separated into passive and semi-active parts. The passive control force components, coupled in structural mode space, are incorporated in the drift coefficients by directly using the stochastic averaging method. Then the stochastic dynamical programming principle is applied to establish a dynamical programming equation, from which the semi-active optimal control law is determined and implementable by MR dampers without clipping in terms of the Bingham model. Under the condition on the control performance function given in section 3, the expressions of nonlinear and linear non-clipped semi-active optimal control force components are obtained as well as the non-clipped semi-active LQG control force, and thus the value function and semi-active nonlinear optimal control force are actually existent according to the developed strategy. An example of the controlled stochastic hysteretic column is given to illustrate the application and effectiveness of the developed semi-active optimal control strategy.

Key Words
nonlinear stochastic optimal control; semi-active optimal control law; MR damper; stochastic averaging; stochastic dynamical programming

Address
Z. G. Ying; Department of Mechanics, Zhejiang University, Hangzhou 310027, P. R. China
Y. Q. Ni and J. M. Ko; Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Abstract
A vibration-impedance-based monitoring method is proposed to predict the loss of prestress forces in prestressed concrete (PSC) girder bridges. Firstly, a global damage alarming algorithm using the change in frequency responses is formulated to detect the occurrence of damage in PSC girders. Secondly, a local damage detection algorithm using the change in electro-mechanical impedance features is selected to identify the prestress-loss in tendon and anchoring members. Thirdly, a prestress-loss prediction algorithm using the change in natural frequencies is selected to estimate the extent of prestress-loss in PSC girders. Finally, the feasibility of the proposed method is experimentally evaluated on a scaled PSC girder model for which acceleration responses and electro-mechanical impedances were measured for several damage scenarios of prestress-loss.

Key Words
structural health monitoring; prestress-loss; vibration-based damage detection; PSC girder; vibration; electro-mechanical impedance; modal parameters.

Address
Jeong-Tae Kim, Jae-Hyung Park, Dong-Soo Hong, Hyun-Man Cho and Won-Bae Na; Department of Ocean Engineering, Pukyong National University, Busan, Korea
Jin-Hak Yi; Korea Ocean Research & Development Institute, Ansan, Korea

Abstract
In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF\'s from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

Key Words
structural health monitoring; damage diagnosis; autoregressive model; hypothesis test; Gaussian mixture model.

Address
Hae Young Noh, K. Krishnan Nair and Anne S. Kiremidjian; Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
C-H. Loh; National Taiwan University, Taipei, Taiwan

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