• Smart Structures and Systems
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• v.4 no.5
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• pp.629-640
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• 2008

A new algorithm is proposed to determine optimal accelerometer locations (OAL) when a structure is identified by frequency domain system identification (SI) method. As a result, a guideline is presented for selecting OAL which can reflect modal response of a structure properly. The guideline is to provide a minimum number of necessary accelerometers with the variation in the number of measurable target modes. To determine OAL for SI applications effectively, the modal sensitivity effective independence distribution vector (MS-EIDV) is developed with the likelihood function of measurements. By maximizing the likelihood of the occurrence of the measurements relative to the predictions, Fisher Information Matrix (FIM) is derived as a function of mode shape sensitivity. This paper also proposes a statistical approach in determining the structural parameters with a presumed parameter error which reflects the epistemic paradox between the determination of OAL and the application of a SI scheme. Numerical simulations have been carried out to examine the proposed OAL algorithm. A two-span multi-girder bridge and a two-span truss bridge were used for the simulation studies. To overcome a rank deficiency frequently occurred in inverting a FIM, the singular value decomposition scheme has been applied.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.8
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• pp.216-223
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• 1999

This study was intended to determine the inertia , damping and stiffness properties of the cab-suspension of agricultural tractors by applying the direct system identification method (DSIM). Since the rigid and elastic modes of the cab-suspension are not likely to be separated clearly, direct application of the DSIM may result in large computation error. To solve such a problem, a method of locating mass center of the cab were determined by assuming the behavior of the cab-suspension is a rigid body motion. The dynamic parameters of the cab-suspension were then determined by applying the DSIM with the known coordinates of the mass center. The constraints of spatial matrices of the cab-suspension also make the algorithm for the DSIM perform better. The values of dynamic parameters determined by this method agreed well with those determined by the experiment.

• Wind and Structures
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• v.14 no.5
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• pp.413-434
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• 2011

Without output covariance estimation, one reference-based Stochastic Subspace Technique (SST) for extracting modal parameters and flutter derivatives of bridge deck is developed and programmed. Compared with the covariance-driven SST and the oscillation signals incurred by oncoming or signature turbulence that adopted by previous investigators, the newly-presented identification scheme is less time-consuming in computation and a more desired accuracy should be contributed to high-quality free oscillated signals excited by specific initial displacement. The reliability and identification precision of this technique are confirmed by a numerical example. For the 3-DOF sectional models of Sutong Bridge deck (streamlined) and Suramadu Bridge deck (bluff) in wind tunnel tests, with different wind velocities, the lateral bending, vertical bending, torsional frequencies and damping ratios as well as 18 flutter derivatives are extracted by using SST. The flutter derivatives of two kinds of typical decks are compared with the pseudo-steady theoretical values, and the performance of $H_1{^*}$, $H_3{^*}$, $A_1{^*}$, $A_3{^*}$ is very stable and well-matched with each other, respectively. The lateral direct flutter derivatives $P_5{^*}$, $P_6{^*}$ are comparatively more accurate than other relevant lateral components. Experimental procedure seems to be more critical than identification technique for refining the estimation precision.

• Wind and Structures
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• v.22 no.3
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• pp.273-290
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• 2016

The flutter derivatives of bridge decks can be efficiently identified using the experimentally and/or numerically coupled forced vibration method. This paper addresses the issue of inherent requirement for adopting different frequencies of three modes in this method. The aerostatic force components and the inertia of force and moment are mathematically proved to exert no influence on identification results if the signal length (t) is integer (n=1,2,3...) times of the least common multiple (T) of three modal periods. It is one important contribution to flutter derivatives identification theory and engineering practice in this study. Therefore, it is unnecessary to worry about the determination accuracy of aerostatic force and inertia of force and moment. The influences of signal length, amplitude, and frequency ratio on flutter derivative are thoroughly investigated using a bridge example. If the signal length t is too short, the extraction results may be completely wrong, and particular attention should be paid to this issue. The signal length t=nT ($n{\geq}5$) is strongly recommended for improving parameter identification accuracy. The proposed viewpoints and conclusions are of great significance for better understanding the essences of flutter derivative identification through coupled forced vibration method.

• Steel and Composite Structures
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• v.27 no.3
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• pp.327-335
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• 2018

This paper presents structural assessment of a steel railway bridge for current condition using modal parameter to upgrade finite element modeling in order to gather accurate result. An adequate monitoring, such as acceleration, displacement, strain monitoring, is important tool to understand behavior and to assess structural performance of the structure under surround vibration by means of the dynamic analysis. Evaluation of conditions of an existing steel railway bridge consist of 4 decks, three of them are 14 m, one of them is 9.7 m, was performed with a numerical analysis and a series of dynamic tests. Numerical analysis was performed implementing finite element model of the bridge using SAP2000 software. Dynamic tests were performed by collecting acceleration data caused by surrounding vibrations and dynamic analysis is performed by Operational Modal Analysis (OMA) using collected acceleration data. The acceleration response of the steel bridge is assumed to be governing response quantity for structural assessment and provide valuable information about the current statute of the structure. Modal identification determined based on response of the structure play significant role for upgrading finite element model of the structure and helping structural evaluation. Numerical and experimental dynamic properties are compared and finite element model of the bridge is updated by changing of material properties to reduce the differences between the results. In this paper, an existing steel railway bridge with four spans is evaluated by finite element model improved using operational modal analysis. Structural analysis performed for the bridge both for original and calibrated models, and results are compared. It is demonstrated that differences in natural frequencies are reduced between 0.2% to 5% by calibrating finite element modeling and stiffness properties.

• Journal of Korean Society of Steel Construction
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• v.20 no.6
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• pp.761-771
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• 2008

When the crossing frequency of a train meets the natural frequency of a railway bridge, the bridge is bound to become resonant. There are few available time response samples involving a train that passes a bridge at high speed. Very effective modal-parameter extraction techniques for such special high-speed railway bridge conditions are introduced in this paper. Utilizing the cross-correlations of the free-vibration responses after the train passes, mode shapes and the temporal modal parameters (e.g., natural frequency and damping ratio) are extracted using the TDD and SI techniques, respectively. This approach has been applied to a two-span steel composite bridge in the Kyung-Bu high-speed railway system. The estimation results were compared with those obtained using the existing methods. The results fully coincide with those that were extracted using the existing aforementioned technique.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.3
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• pp.155-163
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• 2023

Real-time monitoring technology is critical for ensuring the safety and reliability of nuclear power plant structures. However, the current seismic monitoring system has limited system identification capabilities such as modal parameter estimation. To obtain global behavior data and dynamic characteristics, multiple sensors must be optimally placed. Although several studies on optimal sensor placement have been conducted, they have primarily focused on civil and mechanical structures. Nuclear power plant structures require robust signals, even at low signal-to-noise ratios, and the robustness of each mode must be assessed separately. This is because the mode contributions of nuclear power plant containment buildings are concentrated in low-order modes. Therefore, this study proposes an optimal sensor placement methodology that can evaluate robustness against noise and the effects of each mode. Indicators, such as auto modal assurance criterion (MAC), cross MAC, and mode shape distribution by node were analyzed, and the suitability of the methodology was verified through numerical analysis.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.2
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• pp.217-224
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• 2005

A methodology of selecting an optimal model is proposed for applying a frequency-domain SI method effectively. Instead of using a reduced finite element model, a reasonably detail finite element model is established first and then the model is identified. To satisfy the identifiability criterion, a parameter grouping scheme is applied to control the number of unknowns. Among the simulated member grouping cases, an optimal model is selected as the one with the minimal statistical error. The proposed approach has been examined through simulation studies on a single span box-girder bridge.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.743-750
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• 2017

Modal parameter identification plays a key role in the structural health monitoring (SHM) for civil engineering. Eigensystem realization algorithm (ERA) is one of the most popular identification methods. However, the complex environment around civil structures can introduce the noises into the measurement from SHM system. The spurious modes would be generated due to the noises during ERA process, which are usually ignored and be recognized as physical modes. This paper proposes an improved stabilization diagram method in ERA to distinguish the spurious modes. First, it is proved that the ERA can be performed by any two Hankel matrices with one time step shift. The effect of noises on the eigenvalues of structure is illustrated when the choice of two Hankel matrices with one time step shift is different. Then, a moving data diagram is proposed to combine the traditional stabilization diagram to form the improved stabilization diagram method. The moving data diagram shows the mode variation along the different choice of Hankel matrices, which indicates whether the mode is spurious or not. The traditional stabilization diagram helps to determine the concerned truncated order before moving data diagram is implemented. Finally, the proposed method is proved through a numerical example. The results show that the proposed method can distinguish the spurious modes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.5
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• pp.777-782
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• 2002

This study will offer multimodal recognition instead of an existing monomodal bioinfomatics by using facial multi-feature to improve the accuracy of recognition and to consider the convenience of user . Each bioinfomatics vector can be found by the following ways. For a face, the feature is calculated by principal component analysis with wavelet multiresolution. For a lip, a filter is used to find out an equation to calculate the edges of the lips first. Then by using a thinning image and least square method, an equation factor can be drawn. A feature found out the facial parameter distance ratio. We've sorted backpropagation neural network and experimented with the inputs used above. Based on the experimental results we discuss the advantage and efficiency.