• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.327-332
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• 2005

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.759-777
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• 2013

In this paper, a topology optimization method based on the element independent nodal density (EIND) is developed for continuum solids with multiple load cases and multiple constraints. The optimization problem is formulated ad minimizing the volume subject to displacement constraints. Nodal densities of the finite element mesh are used a the design variable. The nodal densities are interpolated into any point in the design domain by the Shepard interpolation scheme and the Heaviside function. Without using additional constraints (such ad the filtering technique), mesh-independent, checkerboard-free, distinct optimal topology can be obtained. Adopting the rational approximation for material properties (RAMP), the topology optimization procedure is implemented using a solid isotropic material with penalization (SIMP) method and a dual programming optimization algorithm. The computational efficiency is greatly improved by multithread parallel computing with OpenMP to run parallel programs for the shared-memory model of parallel computation. Finally, several examples are presented to demonstrate the effectiveness of the developed techniques.

• Smart Structures and Systems
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• v.20 no.1
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• pp.61-74
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• 2017

One of the most reliable and simplest tools for structural vibration control in civil engineering is Tuned Mass Damper, TMD. Provided that the frequency and damping parameters of these dampers are tuned appropriately, they can reduce the vibrations of the structure through their generated inertia forces, as they vibrate continuously. To achieve the optimal parameters of TMD, many different methods have been provided so far. In old approaches, some formulas have been offered based on simplifying models and their applied loadings while novel procedures need to model structures completely in order to obtain TMD parameters. In this paper, with regard to the nonlinear decision-making of fuzzy systems and their enough ability to cope with different unreliability, a method is proposed. Furthermore, by taking advantage of both old and new methods a fuzzy system is designed to be operational and reduce uncertainties related to models and applied loads. To design fuzzy system, it is required to gain data on structures and optimum parameters of TMDs corresponding to these structures. This information is obtained through modeling MDOF systems with various numbers of stories subjected to far and near field earthquakes. The design of the fuzzy systems is performed by three methods: look-up table, the data space grid-partitioning, and clustering. After that, rule weights of Mamdani fuzzy system using the look-up table are optimized through genetic algorithm and rule weights of Sugeno fuzzy system designed based on grid-partitioning methods and clustering data are optimized through ANFIS (Adaptive Neuro-Fuzzy Inference System). By comparing these methods, it is observed that the fuzzy system technique based on data clustering has an efficient function to predict the optimal parameters of TMDs. In this method, average of errors in estimating frequency and damping ratio is close to zero. Also, standard deviation of frequency errors and damping ratio errors decrease by 78% and 4.1% respectively in comparison with the look-up table method. While, this reductions compared to the grid partitioning method are 2.2% and 1.8% respectively. In this research, TMD parameters are estimated for a 15-degree of freedom structure based on designed fuzzy system and are compared to parameters obtained from the genetic algorithm and empirical relations. The progress up to 1.9% and 2% under far-field earthquakes and 0.4% and 2.2% under near-field earthquakes is obtained in decreasing respectively roof maximum displacement and its RMS ratio through fuzzy system method compared to those obtained by empirical relations.

• Structural Engineering and Mechanics
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• v.67 no.6
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• pp.643-658
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• 2018

In this paper, we introduce a new framework for running the finite element (FE) packages inside an online Loop together with MATLAB. Contrary to the Hardware-in-the-Loop techniques (HiL), in the proposed Software-in-the-Loop framework (SiL), the FE package represents a simulation platform replicating the real system which can be out of access due to several strategic reasons, e.g., costs and accessibility. Practically, SiL for sophisticated structural design and multi-physical simulations provides a platform for preliminary tests before prototyping and mass production. This feature may reduce the new product's costs significantly and may add several flexibilities in implementing different instruments with the goal of shortlisting the most cost-effective ones before moving to real-time experiments for the civil and mechanical systems. The proposed SiL interconnection is not limited to ABAQUS as long as the host FE package is capable of executing user-defined commands in FORTRAN language. The focal point of this research is on using the compiled FORTRAN subroutine as a messenger between ABAQUS/CAE kernel and MATLAB Engine. In order to show the generality of the proposed scheme, the limitations of the available SiL schemes in the literature are addressed in this paper. Additionally, all technical details for establishing the connection between FEM and MATLAB are provided for the interested reader. Finally, two numerical sub-problems are defined for offline and online post-processing, i.e., offline optimization and closed-loop system performance analysis in control theory.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.6
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• pp.769-774
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• 2013

Turbine blades and disc, which are one of the most important rotating parts of a gas turbine engine, are required to have highly efficient performance in order to minimize the total life cycle costs. Owing to these requirements, these components are exposed to severe conditions such as extreme turbine inlet temperatures, high compression ratios, and high speeds. To evaluate the structural integrity of a turbine disc under these conditions, material modeling and finite element analysis techniques are essential; furthermore, shape optimization is necessary for determining the optimal solution. This study aims to generate 2D finite element models of an axisymmetry model and a sector one and to perform thermal-structural coupled-field analysis and contact analysis. Structurally vulnerable areas such as the disc bore and disc-blade interface region are analyzed by a parametric study. Finally, an improved design is provided based on the results, and the necessity of elaborate shape optimization is confirmed.

• KDI Journal of Economic Policy
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• v.36 no.3
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• pp.61-93
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• 2014

This paper analyzes Onbid car auction data by employing various methods, including structural estimation, to identify main factors which decides auction prices and figure out what effects those factors are making on the auction price. I then discuss on how to maximize sellers' revenue in OnBid car auctions. The government and public institutes sell their assets through the OnBid auction, hence the optimal design of the OnBid auction is important. The paper's main findings are as follows: (ⅰ) The independent private value model explains OnBid car auction data better than the correlated private value model or the interdependent value model; (ⅱ) Both the number of bidders and the ratios of the auction price to the evaluation value were lower in the auctions posted by the Kamco than auctions by institutes other than the Kamco; (ⅲ) Some auctions require that at least two bidders should submit a bid no less than the reserve price for sale. In those auctions, both the number of bidders and each bidder's valuation on the auctioned object were lower than in auctions without that requirement; (ⅳ) The sum of sellers' revenue would be decreased in the simulation with the reserve price higher by 5%, 10%, and 20% across auctions by institutes other than Kamco.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.26-32
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• 2016

Usually, there are many uncertainties regarding the error of an assumed load, material properties, member size, and structure analysis in a structure, and it may have a direct influence on the qualities of optimal design of structures. Probabilistic analysis has developed rapidly into a desirable process and structural reliability analysis is an increasingly important tool that assists engineers to consider uncertainties during the design, construction and life of a structure to calculate its probability of failure. This study deals with the applications of two optimization techniques to solve the reliability-based optimization problem of structures. The reliability-based optimization problem was formulated as a minimization of the structural volume subject to the constraints on the values of componential reliability index determined by the AFOSM approach. This presented method may be a useful tool for the reliability-based design optimization of structures.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.1
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• pp.31-38
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• 2018

Earthquake disasters that exceed the design criteria can pose significant threats to nuclear facilities. Seismic probabilistic safety assessment(PSA) is a probabilistic way to quantify such risks. Accordingly, seismic PSA has been applied to domestic and overseas nuclear power plants, and the safety of nuclear power plants was evaluated and prepared against earthquake hazards. However, there were few examples where seismic PSA was applied in case of a research reactor with a relatively small size compared to nuclear power plants. Therefore, in this study, seismic PSA technique was applied to actually completed research reactor to analyze its safety. Also, based on these results, the optimization study on the seismic capacity of the system constituting the research reactor was carried out. As a result, the possibility of damage to the core caused by the earthquake hazard was quantified in the research reactor and its safety was confirmed. The optimization study showed that the optimal seismic capacity distribution was obtained to ensure maximum safety at a low cost compared with the current design. These results, in the future, can expect to be used as a quantitative indicator to effectively improve the safety of the research reactor with respect to earthquakes.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.5
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• pp.438-447
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• 2008

This study is focused on reliability based design optimization (RBDO) using moving least squares. A response surface is used to derive a limit-state equation for reliability based design optimization. Response surface method (RSM) with least square method (LSM) or Kriging will be used as a response surface. RSM is fast to make the response surface. On the other hand, RSM has disadvantage to make the response surface of nonlinear equation. Kriging can make the response surface in nonlinear equation precisely but needs considerable amount of computations. The moving least square method (MLSM) is made of both methods (RSM with LSM+Kriging). Numerical results by MLSM are compared with those by LMS in Rosenbrock function and six-hump carmel back function. The RBDO of engine duct of smart UAV is pursued in this paper. It is proved that RBDO is useful tool for aerospace structural optimal design problems.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.