• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.783-791
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• 2004

In this paper we review recent developments in nonlinear time series modeling on both conditional mean and conditional variance. Traditional linear model in conditional mean is referred to as ARMA(autoregressive moving average) process investigated by Box and Jenkins(1976). Nonlinear mean models such as threshold, exponential and random coefficient models are reviewed and their characteristics are explained. In terms of conditional variances, ARCH(autoregressive conditional heteroscedasticity) class is considered as typical linear models. As nonlinear variants of ARCH, diverse nonlinear models appearing in recent literature including threshold ARCH, beta-ARCH and Box-Cox ARCH models are remarked. Also, a class of unified nonlinear models are considered and parameter estimation for that class is briefly discussed.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.975-976
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• 2008

Reliable channel modeling becomes an important measure in performance evaluation on various data detection algorithms. For this reason, correct and accurate modeling is required. This paper presents a nonlinear modeling of Super-RENS (Super-Resolution Near Field Structure) read-out signal using the second-order Volterra model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.127-134
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• 1998

The objectives of this paper is to analyze the reinforced concrete structures by using fiber model. In this study, the fiber modeling techniques including modeling of support conditions are studied. In order to verify the modeling techniques, analysis results obtained for reinforced concrete cantilever beam and reinforced concrete T-girder bridge under cyclic loading are compared with experimental results from full scale test. From the comparison, it is shown that the modeling techniques in this study can be well applied to the nonlinear failure analysis of reinforced concrete structures with porper modifications.

• Structural Engineering and Mechanics
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• v.65 no.5
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• pp.611-619
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• 2018

This paper addresses numerical modeling and nonlinear analysis of unreinforced masonry walls and vaults externally strengthened using fiber reinforced polymers (FRP). The aim of the research is to provide a simple method for design of strengthening interventions for masonry arched structures while considering the nonlinear behavior. Several brick masonry walls and vaults externally strengthened by FRP which have been previously tested experimentally are modeled using finite elements. Numerical modeling and nonlinear analysis are performed using commercial software. Description of the modeling, material characterization and solution parameters are given. The obtained numerical results demonstrate that externally applied FRP strengthening increased the ultimate capacity of the walls and vaults and improved their failure mode. The numerical results are in good agreement with the experimentally obtained ultimate failure load, maximum displacement and crack pattern; which demonstrates the capability of the proposed modeling scheme to simulate efficiently the actual behavior of FRP-strengthened masonry elements. Application is made on a historic masonry dome and the numerical analysis managed to explain its structural behavior before and after strengthening. The modeling approach may thus be regarded a practical and valid tool for design of strengthening interventions for contemporary or historic unreinforced masonry elements using externally bonded FRP.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1439-1461
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• 2015

Design criteria, modeling rules, and analysis principles of seismic isolation systems have already found place in important building codes and standards such as the Uniform Building Code and ASCE/SEI 7-05. Although real behaviors of isolation systems composed of high damping or lead rubber bearings are nonlinear, equivalent linear models can be obtained using effective stiffness and damping which makes use of linear seismic analysis methods for seismic-isolated buildings possible. However, equivalent linear modeling and analysis may lead to errors in seismic response terms of multi-story buildings and thus need to be assessed comprehensively. This study investigates the accuracy of equivalent linear modeling via numerical experiments conducted on generic five-story three dimensional seismic-isolated buildings. A wide range of nonlinear isolation systems with different characteristics and their equivalent linear counterparts are subjected to historical earthquakes and isolation system displacements, top floor accelerations, story drifts, base shears, and torsional base moments are compared. Relations between the accuracy of the estimates of peak structural responses from equivalent linear models and typical characteristics of nonlinear isolation systems including effective period, rigid-body mode period, effective viscous damping ratio, and post-yield to pre-yield stiffness ratio are established. Influence of biaxial interaction and plan eccentricity are also examined.

• Journal of Mechanical Science and Technology
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• v.16 no.7
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• pp.896-901
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• 2002

This paper presents a nonlinear modeling method for dynamic analysis of flexible structures undergoing overall motions that employs the mode approximation method. This method, different from the naive nonlinear method that approximates only Cartesian deformation variables, approximates not only deformation variables but also strain variables. Geometric constraint relations between the strain variables and the deformation variables are introduced and incorporated into the formulation. Two numerical examples are solved and the reliability and the accuracy of the proposed formulation are examined through the numerical study.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.550-555
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• 2001

This paper presents a nonlinear modeling method for dynamic analysis of flexible structures undergoing overall motions that employs the mode approximation method. This method, different from the naive nonlinear method that approximates only Cartesian deformation variables, approximates not only deformation variables but also strain variables. Geometric constraint relations between the strain variables and the deformation variables are introduced and incorporated into the formulation. Two numerical examples are solved and the reliability and the accuracy of the proposed formulation are examined through the numerical study.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2034-2036
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• 2002

In this paper, we propose the modeling of a chaotic nonlinear system using wavelet neural networks. In our modeling, we used the parameter adjusting method as the training method of a wavelet neural network. The difference between the actual output of a nonlinear chaotic system and that of a wavelet neural network adjusts the parameters of a wavelet neural network using the gradient-descent method. To verify the efficiency of this paper, we perform the simulation using Duffing system, which is a representative continuous time chaotic nonlinear system.

• Computers and Concrete
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• v.12 no.4
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• pp.443-498
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• 2013

A detailed finite element modeling is presented for the simulation of the nonlinear behavior of reinforced concrete structures which manages to predict the nonlinear behavior of four different experimental setups with computational efficiency, robustness and accuracy. The proposed modeling method uses 8-node hexahedral isoparametric elements for the discretization of concrete. Steel rebars may have any orientation inside the solid concrete elements allowing the simulation of longitudinal as well as transverse reinforcement. Concrete cracking is treated with the smeared crack approach, while steel reinforcement is modeled with the natural beam-column flexibility-based element that takes into consideration shear and bending stiffness. The performance of the proposed modeling is demonstrated by comparing the numerical predictions with existing experimental and numerical results in the literature as well as with those of a commercial code. The results show that the proposed refined simulation predicts accurately the nonlinear inelastic behavior of reinforced concrete structures achieving numerical robustness and computational efficiency.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.