• Journal of Ocean Engineering and Technology
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• v.24 no.1
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• pp.123-132
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• 2010

This paper presents a prestress-loss monitoring technique for prestressed concrete (PSC) girder structures that uses a vibration-based system identification method. First, the theoretical backgrounds of the prestress-loss monitoring technique and the system identification technique are presented. Second, vibration tests are performed on a lab-scaled PSC girder for which the modal parameter was measured for several prestress-force cases. A numerical modal analysis is performed by using an initial finite element (FE) model from the geometric, material, and boundary conditions of the lab-scaled PSC girder. Third, a vibration-based system identification is performed to update the FE model by identifying structural parameters since the natural frequency of the FE model became identical to the experimental results. Finally, the feasibility of the prestress-loss monitoring technique is evaluated for the PSC girder model by using the experimentally measured natural frequency and numerically identified natural frequency for several prestress-force cases.

• Structural Engineering and Mechanics
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• v.59 no.6
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• pp.1037-1054
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• 2016

Structural parameter evaluation and external force estimation are two important parts of structural health monitoring. But the structural parameter identification with limited input information is still a challenging problem. A new simultaneous identification method in time domain is proposed in this study to identify the structural parameters and evaluate the external force. Each sampling point in the time history of external force is taken as the unknowns in force evaluation. To reduce the number of unknowns for force evaluation the time domain measurements are divided into several windows. In each time window the structural excitation is decomposed by orthogonal polynomials. The time-variant excitation can be represented approximately by the linear combination of these orthogonal bases. Structural parameters and the coefficients of decomposition are added to the state variable to be identified. The extended Kalman filter (EKF) is augmented and selected as the mathematical tool for the implementation of state variable evaluation. The proposed method is validated numerically with simulation studies of a time-invariant linear structure, a hysteretic nonlinear structure and a time-variant linear shear frame, respectively. Results from the simulation studies indicate that the proposed method is capable of identifying the dynamic load and structural parameters fairly accurately. This method could also identify the time-variant and nonlinear structural parameter even with contaminated incomplete measurement.

• Smart Structures and Systems
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• v.15 no.1
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• pp.227-244
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• 2015

This study develops a two stage procedure to identify the structural damage based on the optimized artificial neural networks. Initially, the modal strain energy index (MSEI) is established to extract the damaged elements and to reduce the computational time. Then the genetic algorithm (GA) and artificial neural networks (ANNs) are combined to detect the damage severity. The input of the network is modal strain energy index and the output is the flexural stiffness of the beam elements. The principal component analysis (PCA) is utilized to reduce the input variants of the neural network. By using the genetic algorithm to optimize the parameters, the ANNs can significantly improve the accuracy and convergence of the damage identification. The influence of noise on damage identification results is also studied. The simulation and experiment on beam structures shows that the adaptive parameter selection neural network can identify the damage location and severity of beam structures with high accuracy.

• Coupled systems mechanics
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• v.7 no.5
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• pp.555-581
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• 2018

The moment-resistant steel frames are frequently used as a load-bearing structure of buildings. Global response of a moment-resistant frame structure strongly depends on connections behavior, which can significantly influence the response and load-bearing capacity of a steel frame structure. The analysis of a steel frame with included joints behavior is the main focus of this work. In particular, we analyze the behavior of two connection types through experimental tests, and we propose numerical beam model capable of representing connection behavior. The six experimental tests, under monotonic and cyclic loading, are performed for two different types of structural connections: end plate connection with an extended plate and end plate connection. The proposed damage-plasticity model of Reissner beam is able to capture both hardening and softening response under monotonic and cyclic loading. This model has 18 constitutive parameters, whose identification requires an elaborate procedure, which we illustrate in this work. We also present appropriate loading program and arrangement of measuring equipment, which is crucial for successful identification of constitutive parameters. Finally, throughout several practical examples, we illustrate that the steel structure connections are very important for correct prediction of the global steel frame structure response.

• Smart Structures and Systems
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• v.15 no.3
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• pp.807-830
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• 2015

When a building structure requires both health monitoring system and vibration control system, integrating the two systems together will be cost-effective and beneficial for creating a smart building structure with its own sensors (nervous system), processors (brain system), and actuators (muscular system). This paper presents a real-time integrated procedure to demonstrate how health monitoring and vibration control can be integrated in real time to accurately identify time-varying structural parameters and unknown excitations on one hand, and to optimally mitigate excessive vibration of the building structure on the other hand. The basic equations for the identification of time-varying structural parameters and unknown excitations of a semi-active damper-controlled building structure are first presented. The basic equations for semi-active vibration control of the building structure with time-varying structural parameters and unknown excitations are then put forward. The numerical algorithm is finally followed to show how the identification and the control can be performed simultaneously. The results from the numerical investigation of an example building demonstrate that the proposed method is feasible and accurate.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.135-141
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• 2008

In this paper, the Proper Orthogonal Decomposition (POD) method, which is a statistical analysis technique to find the modal characteristics of a structure, is adapted to identify the modal parameters of a tall chimney structure. A wind force time history, which is applied to the structure, is obtained by a wind tunnel test of a scale down model. The POD method is applied on the wind force induced responses of the structure, and the true normal modes of the structure can be obtained. The modal parameters including, natural frequency, mode shape, damping ratio and kinetic energy of the structure can be estimated accurately. With these results, it may be concluded that the POD method can be applied to obtain accurate modal parameters from the wind-induced building responses.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2681-2683
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• 2005

In this paper, we present the structure of fuzzy neural network(FNN) based on wavelet function, and apply this network structure to the identification of nonlinear systems. For adjusting the shape of membership function and the connection weights, the parameter learning method based on the gradient descent scheme is adopted. And an approach that uses adaptive learning rates is driven via a Lyapunov stability analysis to guarantee the fast convergence. Finally, to verify the efficiency of our network structure. we compare the Identification performance of proposed wavelet based fuzzy neural network(WFNN) with those of the FNN, the wavelet fuzzy model(WFM) and the wavelet neural network(WNN) through the computer simulation.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.353-373
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• 2012

The instrumented Canton Tower is a 610 m high-rise structure, which has been considered as a benchmark problem for structural health monitoring (SHM) research. In this paper, an improved automatic modal identification method is presented based on a natural excitation technique in conjunction with the eigensystem realization algorithm (NExT/ERA). In the proposed modal identification method, damping ratio, consistent mode indicator from observability matrices (CMI_O) and modal amplitude coherence (MAC) are used as criteria to distinguish the physically true modes from spurious modes. Enhanced frequency domain decomposition (EFDD), the data-driven stochastic subspace identification method (SSI-DATA) and the proposed method are respectively applied to extract the modal parameters of the Canton Tower under different environmental conditions. Results of modal parameter identification based on output-only measurements are presented and discussed. User-selected parameters used in those methods are suggested and discussed. Furthermore, the effect of environmental conditions on the dynamic characteristics of Canton tower is investigated.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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• pp.63-74
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• 2011

Credit cards are more convenient than cash of heavy. Therefore, credit cards are used widely in on_line (internet) and off_line in nowadays. To use credit cards on internet is commonly secure because client identification based security card and authentication certificate. However, to use in off_line as like shop, store, department, restaurant is unsecure because of irregular accident. As client identification is not used in off_line use of credit cards, the irregular use of counterfeit, stolen and lost card have been increasing in number recently. Therefore, client identification is urgently necessary for secure card using in off_line. And the method of client identification must be simple, don't take long time, convenient for client, card affiliate and card company. In this paper, we study a reform measure of the structure and transaction process for the safety improvement of a credit cards. And we propose several authentication method of short-and long-term for client identification. In the proposal, the client authentication method by OTP application of smart-phone is efficient nowadays.