• Steel and Composite Structures
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• v.13 no.1
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• pp.15-33
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• 2012

For an axially loaded box-shaped member, the width-to-thickness ratio of the plate elements preferably should not be greater than 40 for Q235 steel grades in accordance with the Chinese code GB50017-2003. However, in practical engineering the plate width-to-thickness ratio is up to 120, much more than the limiting value. In this paper, a 3D nonlinear finite element model is developed that accounts for both geometrical imperfections and residual stresses and the ultimate capacity of welded built-up box columns, with larger width-to-thickness ratios of 60, 70, 80, and 100, is simulated. At the same time, the interaction buckling strength of these members is determined using the effective width method recommended in the Chinese code GB50018-2002, Eurocode 3 EN1993-1 and American standard ANSI/AISC 360-10 and the direct strength method developed in recent years. The studies show that the finite element model proposed can simulate the behavior of nonlinear buckling of axially loaded box-shaped members very well. The width-to-thickness ratio of the plate elements in welded box section columns can be enlarged up to 100 for Q235 steel grades. Good agreements are observed between the results obtained from the FEM and direct strength method. The modified direct strength method provides a better estimation of the column strength compared to the direct strength method over the full range of plate width-to-thickness ratio. The Chinese code and Eurocode 3 are overly conservative prediction of column capacity while the American standard provides a better prediction and is slightly conservative for b/t = 60. Therefore, it is suggested that the modified direct strength method should be adopted when revising the Chinese code.

• Journal of the Korea Institute of Building Construction
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• v.14 no.6
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• pp.631-637
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• 2014

Recently, following the application of modular housing techniques to not only residential sector, but also to business sector, the scope of modular housing market b expanding. In the case of other developed countries, such markets are entering into the maturity stage, though the market in Korea is not fully formed yet. Thus, it is difficult to check its trend to estimated mid- to long-term prospects of the market. In this context, the study predicted demand of the modular housing market by using a non-linear prediction model based on time series analysis. To get the prospects for the modular housing market, the quantity of housing supply was estimated based on the estimated quantity of newly built housings, and assumed that a portion of the supplied quantity would be the demand for modular housings. Based on the assumption of demand for modular housings, several scenarios were analyzed and the prospects of the modular housing market was obtained by utilizing the non-linear prediction model.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.2 s.54
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• pp.105-113
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• 2007

In the current research, an algorithm of performance-based seismic retrofit design of reinforced concrete columns using FRP jacket has been proposed. For exact prediction of the nonlinear flexural analysis or FRP composite RC members, multiaxial constitutive laws of concrete and composite materials have been presented. For seismic retrofit design, an algorithm of direct displacement-based design method (DDM) proposed by Chopra and Goel (2001) has been newly applied to determine the design thickness of FRP jacket in seismic retrofit of reinforced concrete columns. To compare with the displacement coefficient method (DCM), the DDM gives an accurate prediction of the target displacement in highly nonlinear region, since the DCM uses the elastic stiffness before reaching the yield load as the effective stiffness but the DDM uses the secant stiffness.

• Advances in Computational Design
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• v.2 no.3
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.3
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• pp.281-287
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• 2011

This paper explains the basic theory and a new development of for the residual strength prediction program of the asymmetrically damaged ships, being capable of searching moment-curvature relations considering neutral axis mobility. It is noted that moment plane and neutral axis plane should be separately defined for asymmetric sections. The validity of the new program is verified by comparing moment-curvature curves of 1/3 scaled frigate model where the results from new algorithm well coincide with experimental and nonlinear FEA results for intact condition and with nonlinear FEA results for damaged condition. Applicability of new algorithm is also verified by applying VLCC model to the newly developed program. It is proved that reduction of residual strengths is visually presented using the new algorithm when damage specifications of ABS, DNV and IMO are applied. It is concluded that the new algorithm shows very good performance to produce moment-curvature relations with neutral axis mobility on the asymmetrically damaged ships. It is expected that the new program based on the developed algorithm can largely reduce design period of FE modeling and increase user conveniences.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.6
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• pp.46-53
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• 2016

It is required to predict a limit cycle amplitude controlled by system's nonlinear behavior as well as an eigen-frequency and initial growth rate of instabilities under the linear motions, in order to fully understand combustion instabilities in a lean premixed gas turbine combustor. Special focus of the current work is placed on the limit cycle amplitude prediction using flame describing function(FDF) where the ratio of a heat release fluctuation to a given flow perturbation is expressed as a function of frequency and amplitude. In this study, the CFD modeling work based on RANS is carried out to obtain FDF, which makes that the nonlinear thermo-acoustic model is successfully developed for predicting the limit cycle amplitude of the combustion instability.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.9
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• pp.355-360
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• 2016

Continuing to the modeling of heating load, this paper, as the second part of consecutive works, presents LS-SVM (least square support vector machine) based model of winter time apartment hot water supply load in a district heating system, so as to be used in prediction of heating energy usage. Similar, but more severely, to heating load, hot water supply load varies in highly nonlinear manner. Such nonlinearity makes analytical model of it hardly exist in the literatures. LS-SVM is known as a good modeling tool for the system, especially for the nonlinear system depended by many independent factors. We collect 26,208 data of hot water supply load over a 13-week period in winter time, from 12 heat exchangers in seven different apartments. Then part of the collected data were used to construct LS-SVM based model and the rest of those were used to test the formed model accuracy. In modeling, we first constructed the model of district heating system's hot water supply load, using the unit heating area's hot water supply load of seven apartments. Such model will be used to estimate the total hot water supply load of which the district heating system needs to provide. Then the individual apartment hot water supply load model is also formed, which can be used to predict and to control the energy consumption of the individual apartment. The results obtained show that the total hot water supply load, which will be provided by the district heating system in winter time, can be predicted within 10% in MAPE (mean absolute percentage error). Also the individual apartment models can predict the individual apartment energy consumption for hot water supply load within 10% ~ 20% in MAPE.

• Journal of Ocean Engineering and Technology
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• v.33 no.1
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• pp.33-41
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• 2019

Estimating fatigue damage is a very important issue in the design of ships. The springing and whipping response, which is the hydro-elastic response of the ship, can increase the fatigue damage of the ship. So, these phenomena should be considered in the design stage. However, the current studies on the the application of springing and whipping responses at the design stage are not sufficient. So, in this study, a prediction method was developed using fluid-structural interaction analysis to assess of the fatigue damage induced by springing and whipping. The stress transfer function (Stress RAO) was obtained by using the 3D FE model in the frequency domain, and the fatigue damage, including linear springing, was estimated by using the wide band damage model. We also used the 1D beam model to develop a method to estimate the fatigue damage, including nonlinear springing and whipping by the vertical bending moment in the short-term sea state. This method can be applied to structural members where fatigue strength is weak to vertical bending moments, such as longitudinal stiffeners. The methodology we developed was applied to 325K VLOC, and we analyzed the effect of the springing and whipping phenomena on the existing design.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.3
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• pp.165-171
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• 2023

The rotary joint connecting the upper and lower structures of construction machinery and special vehicles transmits hydraulic pressure as the shaft and housing rotate, and multiple seals are assembled to prevent oil leakage into the oil flow channel. Because the seal material is rigid and difficult to assemble, we sought a method to assemble it after cutting. The shapes of the cutting surface are L-shaped and / shaped, and the leakage standard when hydraulic pressure is applied is the contact pressure generated on the cutting surface. The structure and material of the seal are composed of a double elastomer, and nonlinear contact structural analysis is performed when only the high-rigidity PE material is cut. Studies have shown that the shorter the cutting length, the better the leakage prevention and the higher the possibility of leakage to the bottom surface where NBR and PE come into contact rather than the top surface where the PE and the housing come into contact.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.3
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• pp.176-182
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• 2024

In this study, we compared and analyzed various methods of missing data handling to build a machine learning model that can effectively analyze and predict time series data with a high percentage of missing values. For this purpose, Predictive State Model Filtering (PSMF), MissForest, and Imputation By Feature Importance (IBFI) methods were applied, and their prediction performance was evaluated using LightGBM, XGBoost, and Explainable Boosting Machines (EBM) machine learning models. The results of the study showed that MissForest and IBFI performed the best among the methods for handling missing values, reflecting the nonlinear data patterns, and that XGBoost and EBM models performed better than LightGBM. This study emphasizes the importance of combining nonlinear imputation methods and machine learning models in the analysis and prediction of time series data with a high percentage of missing values, and provides a practical methodology.