• Steel and Composite Structures
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• v.23 no.1
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• pp.31-40
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• 2017

This article aims at presenting multi objective optimization of parameters that affect crashworthiness characteristics of bi-tubular structures using Taguchi method with grey relational analysis. To design the experiments, the $L_9$ orthogonal array has been used and based on that, the inner tubes have been fabricated by varying the three influence factors such as reference diameter, length difference and numbers of sides of the polygon with three levels, but all the outer cylinders have the same diameter and length 90 mm and 135 mm respectively. Then, the tailor made bi-tubular steel structures were subjected into quasi static axial compression. From the test results it is found that the crushing behaviors of bi-tubular structures with different combinations were fairly significant. The important responses (crashworthiness indicators) specific energy absorption and crush force efficiency have been evaluated from load - displacement curve. Finally optimal levels of parameters were identified using grey relational analysis, and significance of parameters was determined by analysis of variance. The optimum crashworthiness parameters are reference diameter 80 mm, length difference 0 mm and number of sides of polygon is 3, i.e., triangle within the selected nine bi-tube combinations.

• Steel and Composite Structures
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• v.18 no.3
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• pp.739-756
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• 2015

This paper presents investigations on the hysteretic behavior of concrete-filled circular tubular (CFCT) T-joints subjected to axial cyclic loading at brace end. In the experimental study, four specimens are fabricated and tested. The chord members of the tested specimens are filled with concrete along their full length and the braces are hollow section. Failure modes and load-displacement hysteretic curves of all the specimens obtained from experimental tests are given and discussed. Some indicators, in terms of stiffness deterioration, strength deterioration, ductility and energy dissipation, are analyzed to assess the seismic performance of CFCT joints. Test results indicate that the failures are primarily caused by crack cutting through the chord wall, convex deformation on the chord surface near brace/chord intersection and crushing of the core concrete. Hysteretic curves of all the specimens are plump, and no obvious pinching phenomenon is found. The energy dissipation result shows that the inelastic deformation is the main energy dissipation mechanism. It is also found from experimental results that the CFCT joints show clear and steady stiffness deterioration with the increase of displacement after yielding. However, all the specimens do not perform significant strength deterioration before failure. The effect of joint geometric parameters ${\beta}$ and ${\gamma}$ of the four specimens on hysteretic performance is also discussed.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.4
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• pp.52-64
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• 2019

The purpose of this study is to analyze the impact of macroeconomic variables on air passenger demand and provide useful information to airport managers and policymakers. Therefore, using the quarterly macroeconomic indicators from 2002 to 2017, the relationship with air passenger demand was demonstrated by multiple regression analysis. In the previous studies, they used GDP, Korea Treasury Bond, KOSPI index, USD/KRW Exchange Rate, and WTI Crude Oil Price variables. In this study, we used the Coincident Composite Index, Employment Rate, Consumer Sentiment Index, and Private Consumption Rate used as additional variables. It has confirmed that if the consumption of research results expands or the economic environment is right, it will affect the increase in international passengers. In other words, it confirmed that the overall economic situation acts as the main factor determining air passenger demand. It confirmed that the economic environment at the past has a significant impact on air passenger demand.

• The Journal of Industrial Distribution & Business
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• v.12 no.10
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• pp.31-41
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• 2021

Purpose: This study estimates the threshold values of institutional quality through investigating the non-linear effect of six sub-indices of Worldwide Governance Indicators on FDI inflows in 34 developing countries in Asia and Eastern Europe over the period from 2000-2017. Research Design, data and methodology: GMM EGLS is employed which does not include the lagged value of the dependent variable as an independent variable. As a proxy for the institutional quality, either one of the six sub-indices of WGI from World Bank or the composite index obtained through a principal component analysis is used in a separate model. Results: An improvement in institutional quality, when the quality stays below a certain threshold level, does not increase FDI inflows, and only when the quality is above the threshold, it can positively influence FDI inflows. The threshold values of political stability and absence of violence, government effectiveness, and rule of law are relatively higher than those of the other dimensions of WGI. Conclusion: Institutional quality of the developing economies of Asia and Eastern Europe has a non-linear effect on FDI inflows. The target countries need to upgrade their institutional quality above the threshold in order to attract more FDIs.

• Steel and Composite Structures
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• v.37 no.6
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• pp.679-693
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• 2020

This paper investigates the seismic response of buildings equipped with Self-Centering Energy Dissipating (SCED) braces. Two-dimensional models of 3, 6, 12 and 16-story SCED buildings considering both material and geometric nonlinearities are investigated by carrying out pushover and nonlinear time-history analyses. The response indicators of the buildings are studied for weight-scaled ground motions to represent the Design Basis Earthquake (DBE) level and the Maximum Considered Earthquake (MCE) event. The fragility curves of the buildings for two Immediate Occupancy (IO) and Life Safety (LS) performance levels are developed using Incremental Dynamic Analysis (IDA). Results of the nonlinear response history analyses indicate that the maximum inter-story drift occurs at the taller buildings. The mean peak inter-story drift is less than 2% in both hazard levels. High floor acceleration peaks are observed in all the SCED frames regardless of the building height. The overall ductility and ductility demand increase when the number of stories reduces. The results also showed the residual displacement is negligible for all of case study buildings. The 3 and 6-story buildings exhibit desirable performance in IO and LS performance levels according to fragility curves results, while 12 and 16-story frames show poor performance especially in IO level. The results indicated the SCED braces performance is generally better in lower-rise buildings.

• Steel and Composite Structures
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• v.45 no.2
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• pp.281-291
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• 2022

Surmounting complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) with artificial neural network (ANN) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, the Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.

• Structural Engineering and Mechanics
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• v.89 no.2
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• pp.213-223
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• 2024

This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the LMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.411-426
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• 2011

The construction of composite indicators should be normalized and weighted to render them comparable and evaluable variables in the field, which undergoes absence of a distinct methodology and where the application of universally popular method is common. Constructing of indices does not compare and analyze applying various normalizing and weighting, but constructer generally use chosen method and develops indicators and indices in most research. In this study, indices are applied various normalization and weighting methods, thereby analyzing how much impact the index and identifying individual characteristics derive a more reasonable way to help other research in the future. 5 different methods of normalization and 4 different types of weights were compared and analyzed. There are different results depending applied normalized methods and Z-score method best reflects the characteristics of the variables. According to weighting methods, the calculated results show little difference, but the ranking results of indices did not changed significantly. It might be better to provide constructors with a set of normalization and weighting methods to reflect their characteristics in order to build flood indices through the result of this study.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.78-91
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• 2017

In this study, we conducted a comparative study on user's perception and behavior on public system service (PSS) using institutionalism theory and MGA (multi-group analysis) methodology. In particular, this study focuses on how institutional isomorphism is applied to public system services and how MGA can be implemented correctly in a variance based SEM (structural equation model) such as PLS (partial least square). A data set of 496 effective responses was collected from pubic system users and an empirical research was conducted using three segmented models categorized by public proximity theory (public firms = 113, government contractors = 210, private contractors = 173). For rigorous group comparisons, each model was estimated by the same indicators and approaches. PLS-SEM was used in testing research hypotheses, followed by parametric and non-parametric PLS-MGA procedures in testing categorical moderation effects. This study applied novel procedures for testing composite measurement invariance prior to multi-group comparisons. The following main results and implications are drawn : 1) Partial measurement invariance was established. Multi-group analysis can be done by decomposed models although data can not be pooled for one integrated model. 2) Multi-group analysis using various approaches showed that proximity to public sphere moderated some hypothesized paths from quality dimensions to user satisfaction, which means that categorical moderating effects were partially supported. 3) Careful attention should be given to the selection of statistical test methods and the interpretation of the results of multi-group analysis, taking into account the different outcomes of the PLS-MGA test methods and the low statistical power of the moderating effect. It is necessary to use various methods such as comparing the difference in the path coefficient significance and the significance of the path coefficient difference between the groups. 4) Substantial differences in the perceptions and behaviors of PSS users existed according to proximity to public sphere, including the significance of path coefficients, mediation and categorical moderation effects. 5) The paper also provides detailed analysis and implication from a new institutional perspective. This study using a novel and appropriate methodology for performing group comparisons would be useful for researchers interested in comparative studies employing institutionalism theory and PLS-SEM multi-group analysis technique.