In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

본 연구는 중국 민영기업 최고경영자의 정치적 연계가 경영성과에 미치는 영향을 상층부 이론과 인적 자본 이론을 통해 살펴보았다. 최고경영자의 개인적인 특성은 기업의 전략적인 의사결정에 직간접적인 영향을 미치며, 또한 기업 특유의 장점으로서 지속가능한 성장을 위한 기업의 인적 자본으로도 작용한다. 이에 본 연구는 중국 민영기업 최고경영자가 보유한 정치적 연계가 기업의 인적 자본으로서 경영성과에 긍정적인 영향을 미치는지 실증적으로 확인하고자 하였다. 또한, 정치적 연계의 직접적인 대상인 중국 지방정부를 계층적으로 나누어 정치적 연계라는 인적 자본의 가치와 영향력의 차이 역시 살펴보았으며, 기업의 규모와 연령과 같은 구조적인 특성이 이러한 관계를 조절할 것으로 기대하였다. 이를 검증하기 위해 2008년부터 2016년까지 중국 상하이와 선전 증권거래소에 상장된 1,452개 민영기업 총 9,903개의 관측치를 대상으로 분석하였으며, 그 결과 최고경영자의 정치적 연계는 기업 경영성과에 정(+)의 영향을 미치는 것을 확인할 수 있었다. 또한, 정치적 연계를 계층적인 지방정부로 나누어 살펴보았을 때, 최상위 지방정부인 성급 지방정부와의 정치적 연계만이 경영성과에 긍정적인 영향을 주는 것으로 나타났는데, 이는 최고경 영자가 보유한 인적 자본의 가치와 영향력은 성급 지방정부와 연관이 있을 때만 발휘되는 것으로 볼 수 있다. 또한, 기업 규모가 크고, 기업 연령이 높으면, 성급 지방정부와의 정치적 연계가 경영성과에 미치는 긍정적인 영향이 각각 완화되는 것을 확인할 수 있었다.

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

This paper describes a novel zero-stress member selecting method for sizing optimization of truss structures. When a sizing optimization method with static constraints is implemented, the member stresses are affected sensitively with changing the variables. However, because some truss members are unaffected by specific loading cases, zero-stress states are experienced by the elements. The zero-stress members could affect the computational cost and time of sizing optimization processes. Feature selection approaches can be then used to eliminate the zero-stress member from the whole variables prior to the process of optimization. Several numerical truss examples are tested using the proposed methods.

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

A Beam String Structure (BSS) is a type of hybrid structures, which is composed of upper structural members, lower strings, and struts. Due to the advantages that the pre-tensioned strings elicit pre-caber of the upper structural members, the deflection can be greatly reduced without increasing the structural member size. In this study, a two-way beam string structure is proposed to endure bi-directional loading. The two-way beam string structure consists of two cable parts, namely, sagging and arch-shaped cables. A parametric study is presented aimed at proposing design guide lines of the two-way beam string structures. Numerical finite element analyses through the ABAQUS package were implemented to obtain their behaviors.

A tensegrity module structure is suitable type for spatial structures. Because the tensegrity is composed of set of discontinuous compressive elements (struts) floating within a net of continuous tensile elements (cables), the system can provide the basis for lightweight and strong. However, despite the advantages of tensegrities, design and fabrication of the systems have difficulty because of form-finding methods, pin-connection and the control of prestress. In this paper, the new pin-connection method was invented to make the tensegrity module. The production process and practical implementation of uniformly compressed the tensegrity structures by using a UTM are described. Experiments showed the mechanical response and failure aspects of the tensegrity system.

There has been considerable recent interest in deep learning techniques for structural analysis and design. However, despite newer algorithms and more precise methods have been developed in the field of computer science, the recent effective deep learning techniques have not been applied to the damage detection topics. In this study, we have explored the structural damage detection method of truss structures using the state-of-the-art deep learning techniques. The deep neural networks are used to train knowledge of the patterns in the response of the undamaged and the damaged structures. A 31-bar planar truss are considered to show the capabilities of the deep learning techniques for identifying the single or multiple-structural damage. The frequency responses and the elasticity moduli of individual elements are used as input and output datasets, respectively. In all considered cases, the neural network can assess damage conditions with very good accuracy.

In this study, an algorithm applying deep learning to the truss structures was proposed. Deep learning is a method of raising the accuracy of machine learning by creating a neural networks in a computer. Neural networks consist of input layers, hidden layers and output layers. Numerous studies have focused on the introduction of neural networks and performed under limited examples and conditions, but this study focused on two- and three-dimensional truss structures to prove the effectiveness of algorithms. and the training phase was divided into training model based on the dataset size and epochs. At these case, a specific data value was selected and the error rate was shown by comparing the actual data value with the predicted value, and the error rate decreases as the data set and the number of hidden layers increases. In consequence, it showed that it is possible to predict the result quickly and accurately without using a numerical analysis program when applying the deep learning technique to the field of structural analysis.

This study showed that experimental study of inelastic nonlinear behavior of two-way beam string structures. General large span structures consisting of beam members have large moment and long cross section of area. In order to decrease these excessive moment and deflection, the two-way beam string structures composed of H-Beam, strut, and cable elements were proposed. In the two-way string beam, the cable with the prestress improves force distribution of some weight reduction. Two systems made of structural steel and cables were tested. The nonlinear behaviour of the two-way beam string structures studied by using finite element model and compared to experimental results. The displacement of the LVDT in the center of the beam correspond with the ABAQUS results. 2,200MPa cable can afford to bear breaking load than 1,860MPa cable. The two-way beam string structures is correlated to the finite element model and the experimental results. In consequence, It showed that the system with two-way cables exhibits much better structural performances than H-Beam structures and beam with cable.

본 연구는 설화를 중심으로 한 한국문화교재 단원의 구성 원리와 실례를 살펴 여성결혼이민자의 자녀 교육과 한국 사회 정착에 도움을 주는 데 목적이 있다. 이를 위해 여성결혼이민자와 심층 면담을 실시하고 기존 한국어교재 및 문화 관련 교재의 내용과 구성을 분석하였다. 이를 바탕으로 상호문화주의에 기반한 한국문화 교재 단원을 ‘바보 온달과 평강공주’를 예로 들어 제안하였다. 단원은 크게 엄마, 아빠와 자녀가 협력하여 내용을 이해하는 단계와 한국의 언어문화와 행동문화, 성취문화를 이해하고 나아가 엄마(여성결혼이민자) 나라의 문화를 한국문화와 비교해 보는 확장된 활동 단계로 구분된다. 이 연구는 한국인에게는 대중적이나 결혼이민자들에게는 낯설 수 있는 설화를 제시함으로써 자녀 교육에 도움을 줄 수 있을 것이다. 또한 일방향적으로 한국문화만을 제시하고 있는 기존 한국문화교재와 달리 여성결혼이민자의 모국문화와 한국문화를 비교해 보는 활동을 통해 그들의 문화적 자존감과 정체성을 확립하여, 이들이 한국 사회에 건강하게 정착하는 데 기여할 것이다.

In this study, an advanced form-finding method of tensegrity unit modules is presented to apply on renovation building. Here a fitness function of maximum natural frequency which can lead to a maximum stiffness status was used for a genetic algorithm. To apply the lightweight pin-jointed structure to the renovation project is more economical over to build new structures. In this paper, two types of tensegrity unit are presented to build expanded structures, and their force densities are shown using the proposed form-finding method. The expanded structures which may influence renovation projects are presented by using the tensegrity units.

텐세그리티 구조물은 인장력을 받는 연속된 케이블 안에 압축력을 받는 스트럿이 결합된 형태로 구성된다. 텐세그리티 구조물은 자기 응력 상태를 갖는 프리스트레스 핀 접합 구조물에 속한다. 텐세그리티 구조물 설계의 핵심은 평형 배열상태를 구하는 일명 형상탐색 과정이다. 본 논문에서는 세 가지의 효과적인 텐세그리티 구조물의 형상탐색 기법을 제안하였다. 형상탐색과정을 수행하면 평형상태의 내력 밀도와 그에 대응하는 위상을 얻을 수 있다. 이 때 평형상태를 형성하는 적절한 내력밀도 값을 얻기 위해 유전자 알고리즘을 결합한 내력밀도법이 사용되었다. 수치해석 예제를 통해 제안 알고리즘의 효율성을 입증하였다.

This study was conducted for the purpose of deriving implications by observing the changing patterns and characteristics of the farmland reduction area in urban vicinity with Gimhae city, Gyeongsangnam-do as the subject. In order to achieve this goal, we first examined the problems and possibilities of farmland reduction area in urban vicinity through a theoretical review. Additionally, the characteristics of land use and community were examined for Gimhae city, Gyeongsangnam-do. The results of the study are summarized as follows. First, for 35 years from 1981 to 2015, Gimhae decreased 50.52㎢ of farmland, which is about 17.4 times that of Yoido, and about 69.4% of the decreased farmland area. Second, the decrease in agricultural land has been expanding to the whole of Gimhae City from 1990 to 2010, and has been continuing since 2010 around dong-area. Third, in the farmland reduction area in urban vicinity, the number of settlements increases rapidly, but the aging population also increases. Fourth, the composition of the community is getting complicated with the change of the members. Taken together, it is necessary to manage the area efficiently because rapid change is present in the farmland reduction area in urban vicinity. Based on this, the implications are summarized as follows. First, there is a change in land use due to the reduction of farmland not designated as agricultural development region. Therefore, it is necessary to supplement the farmland-conversion standard. Second, despite the fact that land use management has been carried out, there have been problems such as uncontrolled development due to the development pressure beyond institutional management, and therefore it is necessary to improve the structural defects of the pertinent legal system. Fourth, while the traditional farming activities are decreasing with the decrease of agricultural land area, the increase in farms with secondary jobs and the urban-rural interchanges organization’s efforts can lead to increased visits from outsiders that seek rural tourism and experiential learning.

최근 건설구조물에 대한 FRP의 활용에 관한 연구가 활발히 진행되고 있다. FRP는 단위중량당의 강도와 강성이 기존 건설재료인 강재나 콘크리트에 비해 매우 크고, 부식에 대한 저항성이 뛰어나는 등의 여러 가지 물리적, 화학적 장점이 있다. 이러한 장점을 이용하여 FRP 외양관을 단면의 효율성을 극대화할 수 있고 경제성과 경관에 매우 효과가 큰 스트럿을 가진 PSC 박스거더교의 스트럿 부재의 피복재로 적용하고자 한다. 본 논문에서는 스트럿을 가진 PSC 박스거더에 사용되는 FRP 외양관의 적용성을 평가하기 위하여 이와 관련한 FRP 외양관의 시편실험과 FRP로 피복된 콘크리트 부재의 압축실험을 수행하였으며, 실험결과로부터 콘크리트 강도와 에너지 흡수능력 및 연성이 증진되어 스트럿 부재로써 충분한 안전성을 확보할 수 있음을 확인하였다

본 연구에서는 현장타설과 프리캐스트 방식의 콘크리트 충진 FRP 교각의 성능평가 실험을 수행하였다. 8개의 축소모형 실험체에 대한 준정적 실험을 실시하였으며, 실험변수로는 FRP 두께, 콘크리트 강도, 횡방향 철근비, 직경을 선정하였다. 반복 횡하중에 대한 연성능력을 평가하고 각 시험체의 강성저하에 따른 감쇠비와 파괴양상 등을 비교하였다.