• Journal of the Korean Society for Precision Engineering
• /
• v.23 no.6 s.183
• /
• pp.119-127
• /
• 2006

A non-destructive time domain approach to examine structural damage using parameterized partial differential equations and Galerkin approximation techniques is presented. The time domain analysis for damage detection is independent of modal parameters and analytical models unlike frequency domain methods which generally rely on analytical models. The time history of the vibration response of the structure was used to identify the presence of damage. Damage in a structure causes changes in the physical coefficients of mass density, elastic modulus and damping coefficients. This is a part of our ongoing effort on the general problem of modeling and parameter estimation for internal damping mechanisms in a composite beam. Namely, in detecting damage through time-domain or frequency-domain data from smart sensors, the common damages are changed in modal properties such as natural frequencies, mode shapes, and mode shape curvature. This paper examines the use of beam-like structures with piezoceramic sensors and actuators to perform identification of those physical parameters, and detect the damage. Experimental results are presented from tests on cantilevered composite beams damaged at different locations and different dimensions. It is demonstrated that the method can sense the presence of damage and obtain the position of a damage.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11b
• /
• pp.1162-1165
• /
• 2001

Operational Modal Analysis also known as Output Only Modal Analysis has in the recent years been used for extracting modal parameters of civil engineering structures and is now becoming popular for mechanical structures. The advantage of the method is that no artificial excitation need to be applied to the structure or force signals to be measured. All the parameter estimation is based upon the response signals, thereby minimising the work of preparation for the test. This test case is a controlled lab set-up enabling different parameter estimation methods techniques to be used and compared to the Operational Modal Analysis. For Operational Modal Analysis two different estimation techniques are used： a non-parametric technique based on Frequency Domain Decomposition (FDD), and a parametric technique working on the raw data in time domain, a data driven Stochastic Subspace Identification (SS!) algorithm. These are compared to other methods such as traditional Modal Analysis.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.12 no.1
• /
• pp.5-11
• /
• 2002

Active vibration control of intelligent laminated composite plates is performed experimental1y Laminated composite place is modeled by the system identification method. For the system identification process, the laminated composite place is excited by two piezoelectric actuators with PRBS signals. At the same time, the displacement of the laminated composite plate is measured by a gap sensor. From these excited PRBS signals and the measured displacement sequence, system parameters of the laminated composite plate are estimated using a recursive prediction error method. Model of the laminated composite plate with two piezoeletric actuators is assumed to be the form of ARMAX. From the estimated ARHMAX model, a state space equation of the observable canonical form is obtained. With this state space equation, a controller and an observer for active vibration control is designed using the optimal control method. Controller and observer are implemented on a digital system. Experiments on the vibration control are Performed with changing the outer layer fiber orientation of intelligent composite plates.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2003.04a
• /
• pp.95-102
• /
• 2003

To define an analytical model for a structural system or to assess damage in the system, system identification(SI) methods have been developed and widely applied. The paper presents a method of determining optimal sensor location(OSL) based on the maximum likelihood approach, which is applicable to modal SI methods. To estimate unknown parameters reliably, it is necessary that the information provided by the experiment should be maximized. By applying the Cramer-Rao inequality, a Fisher information matrix in terms of the probability density function of measurements is obtained from a lower bound of the estimation error. The paper also proposes a scheme of determining of OSL on damaged structures by using maximum strain energy factor. Simulation studies have carried out to investigate the proposed OSL algorithm for both undamaged and damaged structures.

• Computational Structural Engineering
• /
• v.2 no.2
• /
• pp.113-119
• /
• 1989

Methods for identification of modal properties of linear structures are presented. The extended Kalman filtering technique is employed. The state equation is formulated by two different ways, namely by the time domain and frequency domain approaches. Verifications are carried out by using the simulated records of ground acceleration and structural response. Then the techniques are applied to the estimation of modal parameters of a scaled model for a 3-story building which is installed on a shaking table.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.917-922
• /
• 1992

Resently there have been increasing interests in adaptive identification and control of flexible structures. In this paper, vector channel lattice filters and their applications to parameter identification of flexible structures are studied. Numerical examples are given to show its performace to estimate the natural frequencies of 5-mass system. It is observed that vector channel lattice filter convetges quickly and identifies modal frequencies even when some of them is unobservable for some measurements. Experimental results demonstrated the ability of the lattice filter to identify the natural frequencies and the damping ratios of cantilever beam and pipe.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.30 no.6
• /
• pp.527-538
• /
• 2010

This paper presents a nondestructive evaluation approach for system identification (SID) of real railway bridges using field vibration test results. First, a multi-phase SID scheme designed on the basis of eigenvalue sensitivity concept is presented. Next, the proposed multi-phase approach is evaluated from field vibration tests on a real railway bridge (Wondongcheon bridge) located in Yangsan, Korea. On the steel girder bridge, a few natural frequencies and mode shapes are experimentally measured under the ambient vibration condition. The corresponding modal parameters are numerically calculated from a three-dimensional finite element (FE) model established for the target bridge. Eigenvalue sensitivities are analyzed for potential model-updating parameters of the FE model. Then, structural subsystems are identified phase-by-phase using the proposed model-updating procedure. Based on model-updating results, a baseline model and a nondestructive evaluation of test bridge are identified.

• Steel and Composite Structures
• /
• v.14 no.4
• /
• pp.367-377
• /
• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• Smart Structures and Systems
• /
• v.15 no.3
• /
• pp.665-682
• /
• 2015

Structural identification or St-Id is 'the parametric correlation of structural response characteristics predicted by a mathematical model with analogous characteristics derived from experimental measurements'. This paper describes a St-Id exercise on Humber Bridge that adopted a novel two-stage approach to first calibrate and then validate a mathematical model. This model was then used to predict effects of wind and temperature loads on global static deformation that would be practically impossible to observe. The first stage of the process was an ambient vibration survey in 2008 that used operational modal analysis to estimate a set of modes classified as vertical, torsional or lateral. In the more recent second stage a finite element model (FEM) was developed with an appropriate level of refinement to provide a corresponding set of modal properties. A series of manual adjustments to modal parameters such as cable tension and bearing stiffness resulted in a FEM that produced excellent correspondence for vertical and torsional modes, along with correspondence for the lower frequency lateral modes. In the third stage traffic, wind and temperature data along with deformation measurements from a sparse structural health monitoring system installed in 2011 were compared with equivalent predictions from the partially validated FEM. The match of static response between FEM and SHM data proved good enough for the FEM to be used to predict the un-measurable global deformed shape of the bridge due to vehicle and temperature effects but the FEM had limited capability to reproduce static effects of wind. In addition the FEM was used to show internal forces due to a heavy vehicle to to estimate the worst-case bearing movements under extreme combinations of wind, traffic and temperature loads. The paper shows that in this case, but with limitations, such a two-stage FEM calibration/validation process can be an effective tool for performance prognosis.

• Smart Structures and Systems
• /
• v.10 no.4_5
• /
• pp.471-483
• /
• 2012

In this paper, the structural health monitoring (SHM) benchmark problem of the Canton tower is studied. Based on the field monitoring data from the 20 accelerometers deployed on the tower, some modal frequencies and mode shapes at measured degrees of freedom of the tower are identified. Then, these identified incomplete modal data are used to update the reduced finite element (FE) model of the tower by a novel algorithm. The proposed algorithm avoids the problem of subjective selection of updated parameters and directly updates model stiffness matrix without model reduction or modal expansion approach. Only the eigenvalues and eigenvectors of the normal finite element models corresponding to the measured modes are needed in the computation procedures. The updated model not only possesses the measured modal frequencies and mode shapes but also preserves the modal frequencies and modes shapes in their normal values for the unobserved modes. Updating results including the natural frequencies and mode shapes are compared with the experimental ones to evaluate the proposed algorithm. Also, dynamic responses estimated from the updated FE model using remote senor locations are compared with the measurement ones to validate the convergence of the updated model.