• Smart Structures and Systems
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• v.18 no.6
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• pp.1251-1267
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• 2016

In this paper, Simple Adaptive Control (SAC) is used to enhance the seismic response of nonlinear tall buildings based on acceleration feedback. Semi-active MR dampers are employed as control actuator due to their reliability and well-known dynamic models. Acceleration feedback is used because of availability, cost-efficiency and reliable measurements of acceleration sensors. However, using acceleration feedback in the control loop causes the structure not to apparently meet some requirements of the SAC algorithm. In addition to defining an appropriate SAC reference model and using inherently stable MR dampers, a modification in the original structure of the SAC is proposed in order to improve its adaptability to the situation in which the plant does not satisfy the algorithm's stability requirements. To investigate the performance of the developed control system, a numerical study is conducted on the benchmark 20-story nonlinear building and the responses of the SAC-controlled structure are compared to an $H_2/LQG$ clipped-optimal controller under the effect of different seismic excitations. As indicated by the results, SAC controller effectively reduces the story drifts and hence the seismically-induced damage throughout the structural members despite its simplicity, independence of structural parameters and while using fewer number of dampers in contrast with the $H_2/LQG$ clipped-optimal controller.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.6-8
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• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.401-413
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• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• Earthquakes and Structures
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• v.11 no.2
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• pp.281-295
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• 2016

This paper presents a study on a non-seismically designed reinforced concrete (RC) frame structure. The structure was a existing three-story office building constructed according to the 1990s practice in Vietnam. The 1/3 scaled down versions of structure was tested on a shake table to investigate the seismic performance of this type of construction. It was found that the inter-story drift and the overall behavior of structure meet the requirements of the actual seismic design codes. Then, nonlinear time history analyses are carried out using the fiber beam- column elements. The comparison between the experimental and simulation results shows the performance of the time history analysis models.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.10a
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• pp.348-356
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• 2003

Recently, the buckling is easy to happen a thin plate and High Tensile Steel is used at the steel structure and marine structure so that it is wide. Especially, the post-buckling is becoming important design criteria in the ship structure to use especially the High Tensile Steel. Consequently, it is important that we grasp the conduct post-buckling behaviour accurately at the stability of the ship structure or marine structure. In this study, examined closely about conduct and snap-through behaviour after initial buckling of thin plate structure which apply compressive load according to various kinds initial deflection shape under all edges simply supported condition that make by buckling formula in each payment in advance rule to place which is representative construction of hull. Analysis method is F.E.M in used ANSYS program and complicated nonlinear behaviour to analyze such as secondary buckling with snap-through behaviour. Nonlinear buckling control is applied between newton-raphson method and arc-length method in this study

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.2
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• pp.95-102
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• 2014

In this paper, a coupling scheme for applying finite element analysis(FEA) programs, such as, LS-DYNA and MIDAS/Civil, to a nonlinear soil structure interaction analysis by the boundary reaction method(BRM) is presented. With the FEA programs, the structure and soil media are discretized by linear or nonlinear finite elements. To absorb the outgoing elastic waves to unbounded soil region as much as possible, the PML elements and viscous-spring elements are used at the outer FE boundary, in the LS-DYNA model and in MIDAS/Civil model, respectively. It is also assumed that all the nonlinear elements in the problem are limited to structural region. In this study, the boundary reaction forces for the use in the BRM are calculated using the KIESSI-3D program by solving soil-foundation interaction problem subjected to incident seismic waves. The effectiveness of the proposed approach is demonstrated with a linear SSI seismic analysis problem by comparing the BRM solution with the conventional SSI solution. Numerical comparison indicates that the BRM can effectively be applied to a nonlinear soil-structure analysis if motions at the foundation obtained by the BRM for a linear SSI problem excluding the nonlinear structure is conservative.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1533-1539
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• 2011

In this paper, a class of nonlinear bilevel programs, i.e. the lower level problem is linear programs, is considered. Aiming at this special structure, we append the duality gap of the lower level problem to the upper level objective with a penalty and obtain a penalized problem. Using the penalty method, we give an existence theorem of solution and propose an algorithm. Then, a numerical example is given to illustrate the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2099-2101
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• 2005

The problem of observer design for a class of Lipschitz nonlinear systems is considered. We propose a new observer design method which takes into account the structure of perturbed nonlinear terms with a scaling factor ${\epsilon}$. An example is provided to demonstrate the usefulness of our result over the existing method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.384-384
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• 2000

In adaptive neuro-control, neural networks are used to approximate the unknown plant nonlinearities. Until now, most of the papers in the field of controller design fur nonlinear system using neural networks considers the affine system with fixed number of neurons. This paper considers nonaffne nonlinear systems and dynamic variation of the number of neurons. Control laws and adaptive laws for weights are established so that the whole system is stable in the sense of Lyapunov.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.1-12
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• 2000

We explore the structure and usefulness of CERES plot as a basic tool for dealing with curvature as a function of the new predictor in nonlinear regression. If a predictor has a nonlinear effect and there are nonlinear relationships among the predictors the partial residual plot and augmented partial residual plot are not able to display the correct functional form of the predictor. Unlike these plots the CERES plot can show the correct from. In situations where nonlinearity exists in two predictors we extend the idea of CERES plot to three dimensions, This is illustrated by simulated data.