• Earthquakes and Structures
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• v.8 no.4
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• pp.915-934
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• 2015

Within the context of Structural Health Monitoring (SHM), it is often the case that structural systems are described by uncertainty, both with respect to their parameters and the characteristics of the input loads. For the purposes of system identification, efficient modeling procedures are of the essence for a fast and reliable computation of structural response while taking these uncertainties into account. In this work, a reduced order metamodeling framework is introduced for the challenging case of nonlinear structural systems subjected to earthquake excitation. The introduced metamodeling method is based on Nonlinear AutoRegressive models with eXogenous input (NARX), able to describe nonlinear dynamics, which are moreover characterized by random parameters utilized for the description of the uncertainty propagation. These random parameters, which include characteristics of the input excitation, are expanded onto a suitably defined finite-dimensional Polynomial Chaos (PC) basis and thus the resulting representation is fully described through a small number of deterministic coefficients of projection. The effectiveness of the proposed PC-NARX method is illustrated through its implementation on the metamodeling of a five-storey shear frame model paradigm for response in the region of plasticity, i.e., outside the commonly addressed linear elastic region. The added contribution of the introduced scheme is the ability of the proposed methodology to incorporate uncertainty into the simulation. The results demonstrate the efficiency of the proposed methodology for accurate prediction and simulation of the numerical model dynamics with a vast reduction of the required computational toll.

• Journal of The Korean Association For Science Education
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• v.37 no.2
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• pp.239-252
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• 2017

The purpose of this study is to examine metamodeling knowledge and its components, which means knowledge about model and modeling required for students and teachers for successful application of modeling in the field of science education based on research literature. For this, we analyzed and categorized major previous studies on modeling and modeling through research literature methods. Metamodeling knowledge aims to recognize models and modeling and is the most crucial element to create a scientific model in scientific modeling practice. The point of view of metamodeling knowledge proposed in this study is categorize nature of model, multiplicity of model, purpose of model, modeling process, and evaluation and revision of model. Students should be able to achieve more in-depth understanding through the awareness of the nature of the model. The development of metamodeling knowledge can facilitate students' science learning.

• Journal of the Korean Chemical Society
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• v.63 no.2
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• pp.102-110
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• 2019

The purpose of this study was to explore the progression levels of science metamodeling knowledge through using questionnaires for 97 students of the gifted in G science academy. As a result of the Rasch model analysis, it was confirmed that the progression levels of the scientific metamodeling knowledge is suitable for the person reliability of 0.71 and the item reliability of 0.96. The progression levels of students' science metamodeling knowledge were classified into 4 stages. First and second levels were considered model to be objective and the third and fourth stages were perceived as subjective. The first level is to view the model as a visual representation of a phenomenon as it is, and the second level is to think that the model corresponds to objective knowledge or theory and is a tool for explanation. The Third level looks at the model as a scientist's exploration tool and fourth level is to think that the model is provisional one and multiple models can coexist in one phenomenon. The progression levels of science metamodeling knowledge of science high school students derived from this study is expected to be used as a reference when constructing a curriculum for science modeling and modeling for gifted students.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.263-269
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• 2016

The purpose of this study is to challenge a computational regression-type problem, that is handling large-size data, in which conventional metamodeling techniques often fail in a practical sense. To solve such problems, regression-type boosting, one of ensemble model techniques, together with bootstrapping-based re-sampling is a reasonable choice. This study suggests weight updates by the amount of the residual itself and a new error decision criterion which constructs an ensemble model of models selectively chosen by rejection limits. Through these ideas, we propose AdaBoost.RMU.R as a metamodeling technique suitable for handling large-size data. To assess the performance of the proposed method in comparison to some existing methods, we used 6 mathematical problems. For each problem, we computed the average and the standard deviation of residuals between real response values and predicted response values. Results revealed that the average and the standard deviation of AdaBoost.RMU.R were improved than those of other algorithms.

• Journal of The Korean Association For Science Education
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• v.40 no.2
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• pp.127-140
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• 2020

This study investigated the influence of an explicit and reflective modeling instruction on the metamodeling knowledge of fourth-graders. Two fourth-grade classes in an elementary school in Seoul were selected and each class was assigned to an experimental group and a control group, respectively. The experimental group was engaged in explicit and reflective modeling instruction, whereas the control group was engaged in implicit modeling instruction. The two groups were surveyed before and after instruction on the basis of five metamodeling knowledge categories: definition, purpose, design/construction, changeability, and multiplicity. The experimental group showed positive changes in model's meaning, examples, purpose, changeability as well as multiplicity. In contrast, fewer students in the control group understood the meaning of the model and modeling. They also showed limited changes in their understandings with regards to the modeling instruction, and could not expand their understanding of the nature of model and modeling. The findings indicate that an explicit and reflective modeling instruction has positive influence on elementary students' metamodeling knowledge.

• Computational Structural Engineering
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• v.36 no.3
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• pp.11-16
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• 2023

• Structural Engineering and Mechanics
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• v.72 no.2
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• pp.203-216
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• 2019

Reliability analysis of composite structures considering random variation of involved parameters is quite important as composite materials revealed large statistical variations in their mechanical properties. The reliability analysis of such structures by the first order reliability method (FORM) and Monte Carlo Simulation (MCS) based approach involves repetitive evaluations of performance function. The response surface method (RSM) based metamodeling technique has emerged as an effective solution to such problems. In the application of metamodeling for uncertainty quantification and reliability analysis of composite structures; the finite element model is usually formulated by either classical laminate theory or first order shear deformation theory. But such theories show significant error in calculating the structural responses of composite structures. The present study attempted to apply the RSM based MCS for reliability analysis of composite shell structures where the surrogate model is constructed using higher order shear deformation theory (HSDT) of composite structures considering the uncertainties in the material properties, load, ply thickness and radius of curvature of the shell structure. The sensitivity of responses of the shell is also obtained by RSM and finite element method based direct approach to elucidate the advantages of RSM for response sensitivity analysis. The reliability results obtained by the proposed RSM based MCS and FORM are compared with the accurate reliability analysis results obtained by the direct MCS by considering two numerical examples.

• Journal of The Korean Association For Science Education
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• v.37 no.4
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• pp.539-552
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• 2017

The purpose of this study is to investigate the trends of model and modeling-related research in science education from 1989 to 2016 in Korea. Eighty-five (85) models and modeling-related journal articles were extracted from the KCI-listed journals and analyzed according to the criteria such as participants, research fields, research design, methods, data collection and elements of metamodeling knowledge. According to research participants, three out of four (3/4) were studied for students and the rest were for teachers. More than half of the studies for students were conducted with middle and high school students. The research fields of models and modeling-related researches in science education were comprised of earth science, chemistry, biology science, physics and science course. With regards to research design, the highest type is qualitative research and followed by hybrid research and quantitative research. According to research methods, the most numerous researches that were analyzed was the effectiveness of program, which was a developed model and modeling-related research. The analysis from the elements of the metamodeling knowledge showed most of model and modeling-related research utilized for the change of scientific concept or understanding.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.103-114
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• 2006

Now distribution centers include an ASRS (Automated Storage and Retrieving System) and automated transfer systems such as conveyors and AGV (Automated Guided Vehicle). These automated distribution centers have lots of parameters to be considered fur operating performance. The general basic parameters in the distribution centers are specifications of storage equipment, system operating rules, configuration of storage area and unit load features. In this paper, an approach using simulation and metamodeling with response Surface method to optimize the design parameters of an automated distribution center model is presented. The simulation based metamodel will constitute an efficient approximation of the system function, and the approximate function will be used to design rapid optimal parameters of the distribution center model. This paper provides a comprehensive framework for economical material flow system design using the simulation and metamodeling.

• Computers and Concrete
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• v.23 no.6
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• pp.433-444
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• 2019

As rice husk ash (RHA) is not produced in controlled manufacturing process like cement, its properties vary significantly even within the same lot. In fact, properties of Rice Husk Ash Based Concrete (RHABC) are largely dictated by uncertainty leading to huge deviations from their expected values. This paper proposes a Robust Cost Optimization (RCO) procedure for RHABC, which minimizes such unwanted deviation due to uncertainty and provides guarantee of achieving desired strength and workability with least possible cost. The RCO simultaneously minimizes cost of RHABC production and its deviation considering feasibility of attaining desired strength and workability in presence of uncertainty. RHA related properties have been modeled as uncertain-but-bounded type as associated probability density function is not available. Metamodeling technique is adopted in this work for generating explicit expressions of constraint functions required for formulation of RCO. In doing so, the Moving Least Squares Method is explored in place of conventional Least Square Method (LSM) to ensure accuracy of the RCO. The efficiency by the proposed MLSM based RCO is validated by experimental studies. The error by the LSM and accuracy by the MLSM predictions are clearly envisaged from the test results. The experimental results show good agreement with the proposed MLSM based RCO predicted mix properties. The present RCO procedure yields RHABC mixes which is almost insensitive to uncertainty (i.e., robust solution) with nominal deviation from experimental mean values. At the same time, desired reliability of satisfying the constraints is achieved with marginal increment in cost.