• 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.

• Ecology and Resilient Infrastructure
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• v.3 no.1
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• pp.35-45
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• 2016

The correlation relationships and their corresponding equations between the geomorphological parameters and the Soil Water Assessment Tool (SWAT) model parameters by Sequential Uncertainty Fitting - version 2 (SUFI-2) algorithm of SWAT Calibration and Uncertainty Programs (SWAT-CUP) were developed at the Seom-river experimental watershed. The parameters of the SWAT model at the Soksa-river experimental watershed were estimated by the developed equations. The SWAT model parameters were estimated by SUFI-2 algorithm of SWAT-CUP with rainfall-runoff data from the Soksa-river experimental watershed from 2000 to 2007. Rainfall-runoff simulation of the SWAT model was carried out at the Soksa-river experimental watershed from 2000 to 2007 for the applicability of the estimated parameters by the developed equations. The root mean square errors (RMSE) between the observed and the simulated rainfall-runoffs using the estimated parameters by developed equations of correlation analysis and the optimum parameters by SUFI-2 of SWAT-CUP were $1.09m^3/s$ and $0.93m^3/s$ respectively at the Soksa-river experimental watershed from 2000 to 2007. Therefore, it is considered that the parameter estimation of the SWAT model by the geomorphological characteristics parameters has applicability.

• Journal of KIISE
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• v.42 no.3
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• pp.367-378
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• 2015

This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new algorithm integrating machine learning with various filtering methods. Some simple but useful features for competition relation extraction are also introduced, and an optimum feature set is proposed. The goal of this paper was to increase the precision of competition relation extraction by combining supervised learning with various filtering methods. Filtering methods were employed for classifying compete relation occurrence, using distance restriction for the filtering of feature pairs, and classifying whether or not the candidate entity pair is spam. For evaluation, a test set consisting of 2,565 sentences was examined. The proposed method was compared with the rule-based method and general relation extraction method. As a result, the rule-based method achieved positive precision of 0.812 and accuracy of 0.568, while the general relation extraction method achieved 0.612 and 0.563, respectively. The proposed system obtained positive precision of 0.922 and accuracy of 0.713. These results demonstrate that the developed method is effective for competition relation extraction.

• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.81-89
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• 1996

It is very important for ultrasonic test method to evaluate the integrity of the class I components in nuclear power plants. However, as the rltrasonic test is affected by internal structures and configurations of test materials, backscattering, that is, time invariant noise is generated in large grain size materials. Due to the above reason, the received signal results in low signal to noise(S/N) ratio. Split spectrum processing(SSP) technique is effective to suppress the grain noise. The conventional SSP technique. however, has been applied to unique algorithm. This paper shows that MPO(minimization and polarity threshold) algorithm which two algorithms are applied simulatancously, was utilized, the signal processing time was shorten by using the new constant-Q SSP with the FIR filter which frequency to bandwidth ratio is constant and the optimum parameters were analysed for the signal processing to longitudinal wave and shear wave with the same requirements of inspection on nuclear power plant site. Moreover, the new ultrasonic test instrument, the reference block of the same product form and material specification, stainless stell test specimens and copper test specimens block of the same fabricated for the application of new SSP technique. As the result of experimental test with new ultrasonic test instrument and test specimens, the signal to noise ratio was improved by appying the new SSP technique.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.3
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• pp.31-38
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• 2005

This paper proposes an implementation method of intelligent characters that can properly adapt to action patterns of opponent characters in fighting games by using genetic algorithm. For this intelligent characters, past actions patterns of opponent characters should be included in the learning process. To verify the effectiveness of the proposed method, two types of experiments are performed and their results are compared. In first experiment(exp-1), intelligent characters consider current action and its step of an opponent character. In second experiment (exp-2), on the other hands, they take past actions of an opponent characters into account additionally. As a performance index, the ratio of score obtained by an intelligent character to that of an opponent character is adopted. Experimental results shows that even if the performance index of exp-1 is better than that of exp-2 at the beginning of stages, but the performance index of exp-2 outperforms that of exp-1 as stages go on. Moreover, optimum solutions are always found in all experimental cases in exp-2. Futhermore, intelligent characters in exp-2 could learn moving actions (forward and backward) and waiting actions for getting more scores through self evolution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.2
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• pp.23-38
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• 1990

In this study, decompositive optimization method of two levels was selected to optimize effectively the geometry of the truss which takes the multi-loading condition, and the allowable stress, bucking stress, displacement and natural frequency constraints into consideration. The algorithm of this study is made up of sectional optimization using the feasible direction method in level 1, and geometrical optimization employing Powell's one-direction search method which menimizes only objictive function in level 2. The results of this study acquired by beenning applied to structural model of the truss are as follows : 1. It is verified that the algorithm of this study effectively converges, independent of the initial geometry of the truss and the applied various constraints. 2. The optimum goemetry of the truss varies more considerably according to the constraints selected. 3. Under the condition of the same design, the weight of the truss can be decreased more considerably by means of optimizing even the geometry of truss than by means of optimizing the section of truss while fixing geometrical configuration of it, even though there might be a little difference according to the initial geometry of the truss and the design condition.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1295-1302
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• 2013

The structural deformed shape is important information to structural analysis. If the sufficient measuring points are secured at the structural monitoring system, reasonable and accurate structural deformation shapes can be obtained and structural analysis is possible using this deformation. However, the accurate estimation of the global structural shapes might be difficult if sufficient measuring points are not secure under cost limitations. In this study, SFSM-LS algorithm, the economic and effective estimation method for the structural deformation shapes with limited displacement measuring points is developed and suggested. In the suggested method, the global structural deformation shape is determined by the superposition of the pre-investigated structural deformed shapes obtained by preliminary FE analyses, with their optimum weight factors which lead minimization of the estimate errors. 2-span continuous bridge model is used to verify developed algorithm and parametric studies are performed. By the parametric studies, the characteristics of the estimation results obtained by the suggested method were investigated considering essential parameters such as pre-investigated structural shapes, locations and numbers of displacement measuring points. By quantitative comparison of estimation results with the conventional methods such as polynomial, Lagrange and spline interpolation, the applicability and accuracy of the suggested method was validated.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.46-51
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• 2014

The most important process of engineering system optimal design is to identify the relationship between the design variables and system response. In case of the system optimization, Response Surface Method (RSM) is widely used. The optimization process of RSM generates the design space using the typical alternative candidates and finds the optimal design point in the generated design space. By changing the optimal point depending on the configuration of the design space, it is important to generate the design space. Therefor in this study, the design space is generated by using the relationship between design variables and system response based on Neuro-Response Surface Method (NRSM). And I try to construct the framework for optimal shape design based on NRSM that the optimum shape can be predicted using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) within the generated design space. In order to verify the usefulness of the constructed framework, we applied the nonlinear mathematical function problem. In this study, we can solve the constraints of time in the optimization process for the engineering problem and effective to determine the optimal design was possible. by using the generated framework for optimal shape design based on NRSM. In the future research, we try to apply the optimization problem for Naval Architectural & Ocean Engineering based on the results of this study.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1171-1180
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• 2018

Korea's water transmission system is operated by the nonpressure flow method that flows from highlands to lowlands due to the nature of Korea with many mountainous areas. In order to store water in the highlands, the water pumps are installed and operated. However, In this process, a lot of electrical energy is consumed. therefore, it is necessary to minimize the energy consumption by optimizing the size and operation schedule of the water pumps. The optimal capacity and operation method of the water pump are affected by the size of the tank (distributing reservoir). Therefore, in order to economically design and operate the water transmission system, it is reasonable to consider both the construction cost of the water pump and the tank and the long-term operation cost of the water pump at the step of determining the scale of the initial facilities. In this study, the optimum design model was developed that can optimize both the optimal size of the water pump and the tank and the operation scheduling of the water pump by using the genetic algorithm (GA). The developed model was verified by applying it to the water transmission systems operated in Korea. It is expected that this study will help to estimate the optimal size of the water pump and the tank in the initial design of the water transmission system.