• 한국BIM학회 논문집
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• 제4권3호
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• pp.1-9
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• 2014

As BIM application continues to increase in civil engineering, in this study, 3D information model for RC(Reinforced Concrete) bridge pylon was developed and verified its effectiveness at the structural-design stage. To define 3D information model of RC A-Type pylon, characteristics of pylon were analyzed and 3D model structure was constructed. The 3D information model, one of the core product of BIM, manages all information generated during all life-cycle of a structure and consequently maximizes the efficiency of utilizing information. Also, this study proposes interface module between input data in structural analysis and 3D model of RC pylon. The module can create the input data for non-linear structural analysis. It is essential to study on method of developing 3D information model and propose a structural analysis model by utilizing 3D model for the effective use of BIM techniques in construction industry. The results of this study can be used as the base data for developing the 3D information model of RC pylon in the structural analysis field.

• 한국전산구조공학회논문집
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• 제28권6호
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• pp.579-588
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• 2015

구조실험을 위한 데이터 모델은 구조실험에 관련된 실험정보를 정형화하여 표현하므로 데이터 저장소를 개발하는데 이용할 수 있다. 데이터 모델은 특히 대규모의 구조실험정보 또는 일반적인 다양한 실험정보를 위한 데이터 저장소에 효과적인데 예를 들면 NEES에서 개발한 NEEShub Project Warehouse가 있다. 본 논문은 데이터 모델의 구성과 사용을 평가하기 위한 평가요소를 제안하고 있다. 클래스의 속성이 값을 갖는지를 의미하는 AVE(attribute value existence)란 용어를 도입하여 속성의 사용성에 대한 Attribute AVE, 클래스의 사용성에 대한 Class AVE, 하위레벨에 있는 클래스를 포함하는 Class Level AVE, 하나의 프로젝트의 모든 클래스를 포함하는 Project AVE, 모든 프로젝트를 포함하는 데이터 모델에 대한 Data Model AVE를 정의하였다. 이러한 평가요소들을 NEES 데이터 모델의 프로젝트들에 적용하였는데 데이터 모델내의 클래스와 객체에 대한 사용성을 수치적으로 기술하여 평가하는 것이 가능하였다.

• Journal of Ship and Ocean Technology
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• 제10권4호
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• pp.24-33
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• 2006

So far, the generation of a hull structural analysis model, that is, a finite element model of a hull structure, has been manually performed by a designer using design experience, and thus has required lots of time because of many constraints, the complexity, and the huge size of the hull structure. To make this task automatic, an algorithm for generating the hull structural analysis model is developed using the seam information of the hull structure. A generating system of the hull structural analysis model is implemented based on the developed algorithm. The applicability of the developed algorithm is demonstrated by applying it to the generation of the global and hold structural analysis models of a deadweight 300,000 ton VLCC (Very Large Crude oil Carrier). The results show that the developed algorithm can quickly generate these models at the initial design stage.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1995년도 봄 학술발표회 논문집
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• pp.160-169
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• 1995

This study presents an application of object-oriented methodology for structural dcsign process. A prototype system of integrated a structural design system is developed by introducing a structural analysis object model(SAOM) and structural design object model(SDOM). The SAOM module. which is modeled as a part of structural member, performs structural analysis using FEM approach and the SDOM module checks structural members based on Korea steel design standard. Above mentionedmodelsareabstraclencapsulatibleandreusable.

• 대한조선학회논문집
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• 제44권3호
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• pp.314-322
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• 2007

In general, the vibration and structural analyses have been carried out by using each finite element model separately because of different size of finite element mesh and different focusing area of each analysis. In some cases, however, it is required to perform both global vibration and structural analyses at the same time using a finite element model for global structural analysis, which asks for a special treatment for a vibration analysis. In this study, a technique to perform a global vibration analysis using a finite element model for a global structural analysis has been developed and its effectiveness has been verified by its application to a whole ship.

• 전산구조공학
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• 제9권2호
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• pp.163-171
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• 1996

구조물 모니터링 시스템의 전산환경을 구성하기 위해 필요한 지식 및 정보를 파악하고 이를 지식기반화하는 방법을 제시하였다. 전산환경의 구축을 위한 정보로는 센서 및 하드웨어, 신호처리, 그리고 손상발견/평가를 위한 지식등이 필요한데, 이들은 모두 다른 형태의 지식이므로, -즉 수학연산, 서술적 지식, 수치모델등- 어느 특정의 모델링 기법 단독으로는 이들을 효과적으로 수용하기가 매우 어렵다. 이를 해결하기 위하여 객체지향적 모델링기법과 논리언어를 혼합사용하는 방법 (Hybrid Modeling Paradigm)이 제시되었고, 이의 타당성 및 효율성 검증을 위해 모델구조물을 이용한 예제를 수행하였다.

• 한국광물학회지
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• 제4권2호
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• pp.90-98
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• 1991

This study introduces a new structural model of 1M 2:1 trioctahedral clay minerals or, more generally, 2:1 trioctahedral phyllosilicates. The structural model requires only the chemical formulae of the clay minerals as an input and uses the regression relation (Radoslovich, 1962) to calculate the a- and b-dimensions of the phyllosilicates with the given chemical formulae. The atomic coordinates of the constituent atoms are geometrically calculated for C2/m space group under the assumption that the interatomic distances are constant. To determine the c-dimension, this study calculates the binding energies of 1M 2:1 trioctahedral phyllosilicates as a function of d(001) and find the minimum energy producing d(001). The structural model generates the cell dimensions, interaxial angles, interatomic distances, octahedral, tetrahedral and interlayer thickness, polyhedron deformation angles and atomic coordinates in the unit cell. The simulated structural parameters of phlogopite and annite are very close to the reported data by Hazen and Burnham (1973), suggesting that the structure simulation using only the chemical formule is successful, and thus, that the structural model of this study overcomes the difficulties in the previous models by other investigators.

• Smart Structures and Systems
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• 제29권2호
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Smart Structures and Systems
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• 제32권1호
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2001년도 가을 학술발표회 논문집
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• pp.151-158
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• 2001

This paper describes an example of developing an integrated design system, Integrated Structural Design System for Reinforced Concrete Buildings(INDECON). INDECON incorporates a central database and three design modules: a preliminary design module(PDM), a structural analysis module(SAM), and a detailed design module(DDM). The development of INDECON begins with the development of design models including Design Object Model(DOM) which describes design data during the structural design process. The Design Object Model is transformed to Design Table Model(DTM) for the central database, and is specified to be in detail for the three design modules. Then the central database is implemented and managed by relational database management system(RDBMS), and the three design modules are implemented using C++ programming language. The central database in the server computer communicates with the design modules in the client computers using TCP/IP internet protocol. The developing procedure for INDECON in this paper can be applied for developing more comprehensive integrated structural design systems.