• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1645-1648
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• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of recurrent neural networks model (RNNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of RNNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1560-1563
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• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of support vector machine neural networks model (SVM-NNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of SVM-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.151-157
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• 2003

In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of 'NARMAX' form is employed to consider these effects. A numerical example is carried out to verify the developed bushing model.

• Computers and Concrete
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• v.1 no.2
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• pp.211-226
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• 2004

A relatively simple multiple-vertical-line-element macro model has been incorporated into a standard computer code DRAIN-2D. It was used in blind predictions of seismic response of cantilever RC walls subjected to a series of consequent earthquakes on a shaking table. The model was able to predict predominantly flexural response with relative success. It was able to predict the stiffness and the strength of the pre-cracked specimen and time-history response of the highly nonlinear wall as well as to simulate the shift of the neutral axis and corresponding varying axial force in the cantilever wall. However, failing to identify the rupture of some brittle reinforcement in the third test, the model was not able to predict post-critical, near collapse behaviour during the subsequent response to two stronger earthquakes. The analysed macro model seems to be appropriate for global analyses of complex building structures with RC structural walls subjected to moderate/strong earthquakes. However, it cannot, by definition, be used in refined research analyses monitoring local behaviour in the post critical region.

• Earthquakes and Structures
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• v.25 no.3
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• pp.161-175
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• 2023

High-damping rubber bearings (HDRB) are commonly used as seismic isolation devices to protect civil engineering structures from earthquakes. However, the nonlinear hysteresis characteristics of the HDRB, such as their dependence on material properties and hardening phenomena, make predicting their behavior during earthquakes difficult. This study proposes a hysteretic model that can accurately predicts the behavior of shear deformation considering the nonlinearity when designing the seismic isolation structures using HDR bearings. To model the hysteretic characteristics of the HDR, dynamic loading tests were performed by applying sinusoidal and random waves on scaled-down specimens. The test results show that the nonlinear characteristics of the HDR strongly correlate with the shear strain experienced in the past. Furthermore, when shear deformation occurred above a certain level, the hardening phenomenon, wherein the stiffness increased rapidly, was confirmed. Based on the experimental results, the dynamic characteristics of the HDR, equivalent stiffness, equivalent damping ratio, and strain energy were quantitatively evaluated and analyzed. In this study, an improved bilinear HDR model that can reproduce the dependence on shear deformation and hardening phenomena was developed. Additionally, by proposing an objective parameter-setting procedure based on the experimental results, the model was devised such that similar parameters could be set by anyone. Further, an actual dynamic analysis could be performed by modeling with minimal parameters. The proposed model corresponded with the experimental results and successfully reproduced the mechanical characteristics evaluated from experimental results within an error margin of 10%.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.3
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• pp.53-60
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• 2008

Recently, various recording technologies are studied for optical data storage. After standardization of BD (Blue-ray Disc) and HD-DVD (High-Definition Digital Versatile Disc), the industry is looking for a suitable technology for next generation optical data storage. Super-RENS (Super-resolution near field structure) technique, which is capable of compatibility with other systems, is one of next optical data storage. In this paper, we analyze the nonlinearity of Super-RENS read-out signal through the bicoherence test, which uses HOS (Higher-Order Statistics) and apply neural networks for nonlinear modeling. The model structure considered in this paper is the NARX (Nonlinear AutoRegressive eXogenous) model. The experiment results indicate that the read-out signals have nonlinear characteristics. In addition, it verified the possibility that neural networks can be utilized for nonlinear modeling of Super-RENS systems.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.2
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• pp.123-136
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• 2004

Terzaghi's one-dimensional consolidation theory has some important assumption, which can't be applicable to predict the behavior of soft clay ground. Especially, predictions using infinitesimal strain and linear material function related with permeability can give rise to mistake in comparison with the result of real behavior in site. For this reason, Gibson et al. established a rigorous formulation for the one-dimensional nonlinear finite strain consolidation theory, which can consider non-linearity of material function. But it is difficult to apply this theory to predict the behavior of common soft clay ground with vertical drain. In this study, consolidation model which can consider the vertical and horizontal flow of a fully saturated clay layer, self-weight of soil and nonlinear characteristics of compressibility and permeability are derived. Numerical analysis scheme, which can be applied to consolidation analysis by derived consolidation model in this study was developed. The characteristics of material function were examined using laboratory testing such as standard consolidation test, Rowe-cell test and modified consolidation test.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.6
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• pp.23-32
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• 2007

This study developed an analytical model for predicting nonlinear behavior of PT flat plate frames having slab-column connections with and without slab bottom reinforcement passing through the column. The developed model can predict the failure sequence until punching failure occurs. For verifying the analytical model, the test results of PT flat plate slab-column connections were compared with the results of the analysis. Moreover, the results of static pushover test and shaking table test of 2 story PT flat plate frame were compared with analysis results. For evaluating seismic performance of PT flat plate frame, this study conducted nonlinear response history analysis of the 2 story PT flat plate frame with and without slab bottom reinforcement passing through the column under 1940 El Centro ground motion scaled to have pseudo spectral acceleration of 0.3, 0.5, and 0.7g at the fundamental period of the frame. This study observed that as ground motion is more intense, seismic demands for the frame having the connections without slab bottom reinforcement passing through the column are larger than those without slab bottom reinforcement.

• Journal of Korean Society of Steel Construction
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• v.11 no.2 s.39
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• pp.143-151
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• 1999

An analytical study on the behavior of composite beams, which are composed of cold-formed profiled steel sheeting and normal strength concrete, is described. An analytical method to trace the nonlinear behavior of a composite beam is developed to include the nonlinear material properties of steel sheeting, reinforcing steel bar and concrete. A simple Power Model has also been proposed for the nonlinear moment-curvature relation of the composite beam. The model, which has been originally used to predict the flexural capacity of the beam to column connections, is adapted to the composite beams. The load-deflection behavior of the beams has been simulated by the step-by-step numerical integration using the moment-curvature relation obtained by the Power Model. The results have been compared with test results.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.1404-1414
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• 2008

In the mechanistic-empirical trackbed design of railways, the resilient modulus is the key input parameter. This study focused on the resilient modulus prediction model, which is the functions of mean effective principal stress and axial strain, for three types of railroad trackbed materials such as crushed stone, weathered soil, and crushed-rock soil mixture. The model is composed with the maximum Young's modulus and nonlinear values for higher strain in parallel with dynamic shear modulus. The maximum values is modeled by model parameters, $A_E$ and the power of mean effective principal stress, $n_E$. The nonlinear portion is represented by modified hyperbolic model, with the model parameters of reference strain, ${\varepsilon}_r$ and curvature coefficient, a. To assess the performance of the prediction models proposed herein, the elastic response of a test trackbed near PyeongTaek, Korea was evaluated using a 3-D nonlinear elastic computer program (GEOTRACK) and compared with measured elastic vertical displacement during the passages of freight and passenger trains. The material types of sub-ballasts are crushed stone and weathered granite soil, respectively. The calculated vertical displacements within the sub-ballasts are within the order of 0.6mm, and agree well with measured values with the reasonable margin. The prediction models are thus concluded to work properly in the preliminary investigation.