• Bulletin of the Society of Naval Architects of Korea
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• v.23 no.3
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• pp.10-16
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• 1986

This paper deals with a simple, approximate formula to predict the natural frequencies of the sagged cable structures. Assuming that the propagation velocity of the lateral wave is dependent only on the local mass per unit length and local tension, the explicit simple formula to predict the fundamental period is newly derived. The modified form of these formula is also presented for the prediction of the fundamental period of general shaped cable structures. The results of comparison shows fairly good agreements with experimental results and with theoretical ones. This formula is also used to predict the natural frequencies of a long vertical cable and the derived approximate formula in that case, becomes identical to the exact solution.

• Structural Engineering and Mechanics
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• v.34 no.2
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• pp.159-174
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• 2010

Elastic buckling load of perforated steel plates is typically predicted using the finite element or conjugate load/displacement methods. In this paper an artificial neural network (ANN)-based formula is presented for the prediction of the elastic buckling load of rectangular plates having a circular cutout. By using this formula, the elastic buckling load of perforated plates can be calculated easily without setting up an ANN platform. In this study, the center of a circular cutout was chosen at different locations along the longitudinal x-axis of plates subjected to linearly varying loading. The results of the finite element method (FEM) produced by the commercial software package ANSYS are used to train and test the network. The accuracy of the proposed formula based on the trained ANN model is evaluated by comparing with the results of different researchers. The results show that the presented ANN-based formula is practical in predicting the elastic buckling load of perforated plates without the need of an ANN platform.

• Steel and Composite Structures
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• v.33 no.2
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• pp.181-194
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• 2019

This paper presents an efficient approach to generate a new empirical formula to predict the axial compression capacity (ACC) of circular concrete-filled tube (CCFT) columns using the artificial neural network (ANN). A total of 258 test results extracted from the literature were used to develop the ANN models. The ANN model having the highest correlation coefficient (R) and the lowest mean square error (MSE) was determined as the best model. Stability analysis, sensitivity analysis, and a parametric study were carried out to estimate the stability of the ANN model and to investigate the main contributing factors on the ACC of CCFT columns. Stability analysis revealed that the ANN model was more stable than several existing formulae. Whereas, the sensitivity analysis and parametric study showed that the outer diameter of the steel tube was the most sensitive parameter. Additionally, using the validated ANN model, a new empirical formula was derived for predicting the ACC of CCFT columns. Obviously, a higher accuracy of the proposed empirical formula was achieved compared to the existing formulae.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.635-644
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• 2005

This paper is. a study on relation between road traffic noise(RTN) and sound power level(PWL). At present, many experimental formulae and prediction formulae are used for prediction of RTN. But these formulae are difficult to appiy to the metropolitan area because these formulae are inaccurate in the different condition from reference condition. This paper calculate RTN and PWL of each prediction formula, choose the best one and make a noise map of the subject area. Procedure is as follows. First, calculate $L_{eq}$ of RTN using experimental formulae and prediction formulae. Second, calculate PWL using $L_{eq}$ of RTN and distance attenuation for point source at semi-free field. Third, choose the most accurate formula. And finally, make a noise map of the subject area at present and future. The result using noise map will be able to apply to application field. Noise mapping tool used on this paper is Raynoise program using Ray Tracing Method(RTM), Mirror Image Source Method(MISM) and Hybrid Method(HM).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.505-511
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• 2014

The EPDM(ethylene-propylene diene monomer) rubbers used for manufacturing engine radiator hoses can be degraded by locally generated electrical stress in addition to thermal and mechanical stresses. This study presents an accelerated life prediction of the EPDM rubber under electrochemical stresses using the Arrhenius formula under various aging temperatures($60^{\circ}C$, $80^{\circ}C$, and $100^{\circ}C$). The modified life prediction formula considers the relationship between the gradient($E_a/R$) and the Arrhenius constant(C). The effects of tensile strain(5%, 10%) on the life of these rubbers were investigated. The aging temperature influences EPDM rubber life, and tensile strain was predicted. It was confirmed that the modified life prediction was within the data deviation level of the test.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.477-478
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• 2010

It was in the mid-1980s when the shipboard noise analysis was introduced to the Korean shipbuilding industry. Since then through the continued efforts of the industries in the last decades, native computational codes dedicated to the shipboard noise prediction have been developed based on empirical formula and/or sophisticated theories such as SEA and PFM. This paper addresses some problems in dealing with predicting shipboard noise and the way how to overcome the uncertainties in the prediction.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.717-726
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• 2006

This study is to investigate the MAE of prediction value by collaborative filtering algorithm originated by GroupLens and improved algorithm. To decrease the MAE on the collaborative recommender system on user based, this research proposes the improved algorithm, which reduces the possibility of over estimation of active user's preference mean collaboratively using other user’s preference mean. The result shows the MAE of prediction by improved algorithm is better than original algorithm, so the active user's preference mean used in prediction formula is possibly over estimated.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.5
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• pp.545-554
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• 1999

We would like to develop a short-term model to predict the time-related concentration of ozone whose reaction mechanism is complex. The paper targets Seoul where an ozone alert system has recently been employed. In order to develop a short-term prediction model for ozone, we suggested the Ozone Peak Indicator(OPI), an equivalent of the potential daily maximum ozone concentration, with precursors being the only limiting factor, and we calculated the Ozone Peak Indicarot as OPI={$rac{(O_3)_{max}cdot(H_{eH})_{max}(Rad)_{max}$ to preclude the influence of mixing height and solar radiation on the daily maximum ozone concentration. The OPI on the day of the prediction is to be calcultated by using the relation between OPI and the initial value of precursors. The basic prediction formula for time-related ozone concentration was established as $O_3(1)={(OPI)cdot Rad(t-2)H_{eH}}$, using the OPI, solar radiation two hours before prediction and mixing height. We developed, along with the basic formula for predicting photochemical oxidants, "SEOM"(Seoul Empirical Oxidants Model), a Fortran program that helps predict solar radiation and mixing height needed in the prediction of ozone pollution. When this model was applied to Seoul and an analysis of the correlation between the observed and the predicted ozone concentrations was made through SEOM, there appeared a very high correlation, with a coefficient of 0.815. SEOM can be described as a short-term prediction model for ozone concentration in large cities that takes into account the initial values of precursors, and changes in solar radiation and mixing height. SEOM can reflect the local characteristics of a particular and region can yield relatively good prediction results by a simple data input process.t process.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.4
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• pp.103-111
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• 1985

Presented is a study on the determination of crack width and crack spacing in the reinforced concrete flexural members. The derivation of crack width and crack spacing is based on the recently developed cracking theory. The new prediction formulas for the crack widths and crack spacings are proposed. An experiment for the reinforced concrete beams was conducted to compare with the proposed formulas. The comparisons of the present prediction formulas with our tests and other test data show good agreement. The present crack width formula has been also compared with the well-known ACI formula originally proposed by Gergely & Lutz. It was found that the present crack width formula shows better correlation with test data than that of Gergely & Lutz.

• Journal of Environmental Impact Assessment
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• v.14 no.1
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• pp.25-36
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• 2005

Actually, prediction formula of road traffic noise for EIA(Environmental Impact Assessment) has been used that proposed by National Institute of Environmental Research in 1999. The prediction formula, however, was calculated predicted noise level according to noise level producing on first floor, then needs to correct noise level at each floor in the case of apartment building. The investigation was carried out to calculate the correction coefficient for commonly using in EIA of large scaled apartment development areas. The noised level at each floor were measured from August 2001 to March 2002 at 31 investigation points of large scaled apartment development area in national wide. Measured data were divided and treated with 4 types as 3th floor, 5th floor, 7th floor and 10th floor and then the correction coefficients of each floor were calculated using by correlation formula according to each floor.