• Proceedings of the Korea Concrete Institute Conference
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• 1997.04a
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• pp.392-397
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• 1997

In this paper, design and analysis of anchorage zone in prestressed concrete structure using nonlinear strut and tie model is presented. Nonlinear strut and tie model is an analysis and design model which constructs strut and tie model based on nonlinear analysis considering the nonlinear behavior of concrete. Based on the nonlinear strut and tie model, the analysis and design are performed for the anchorage zone having singular concentric tendons, singular eccentric tendons and multiple tendons, respectively. For verification of the model, comparisons are made with experimental results as well as results by linear strut and tie models. from the comparisons, it is shown that the design of the anchorage zone by the nonlinear model is still economical without loosing the degree of safety and the prediction of the ultimate load by the nonlinear model gives better accuracy than by the linear one.

• Journal of Mechanical Science and Technology
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• v.15 no.11
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• pp.1507-1516
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• 2001

An analytical method is presented for evaluation of the steady state periodic behavior of nonlinear structural systems. This method is based on the substructure synthesis formulation and a harmonic balance procedure, which is applied to the analysis of nonlinear responses. A complex nonlinear system is divided into substructures, of which equations are approximately transformed to modal coordinates including nonlinear term under the reasonable procedure. Then, the equations are synthesized into the overall system and the nonlinear solution for the system is obtained. Based on the harmonic balance method, the proposed procedure reduces the size of large degrees-of-freedom problem in the solving nonlinear equations. Feasibility and advantages of the proposed method are illustrated using the study of the nonlinear rotating machine system as a large mechanical structure system. Results obtained are reported to be an efficient approach with respect to nonlinear response prediction when compared with other conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.10 no.1
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• pp.20-29
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• 1991

A fundamental assumption in conventional linear predictive coding (LPC) analysis procedure is that the input to an all-pole vocal tract filter is white process. In the case of periodic inputs, however, a pitch bias error is introduced into the conventional LP coefficient. Multi-pulse (MP) LP analysis can reduce this bias, provided that an estimate of the excitation is available. Since the prediction error of conventional LP analysis can be modeled as the sum of an MP excitation sequence and a random noise sequence, we can view extracting MP sequences from the prediction error as a classical detection and estimation problem. In this paper, we propose an algorithm in which the locations and amplitudes of the MP sequences are first obtained by applying a likelihood ratio test (LRT) to the prediction error, and LP coefficients free of pitch bias are then obtained from the MP sequences. To verify the performance enhancement, we iterate the above procedure with adaptive threshold at each step.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.550-557
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• 2005

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.311-321
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• 2017

Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.837-851
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• 2013

In construction industry, strength is a primary criterion in selecting a concrete for a particular application. The concrete used for construction gains strength over a long period of time after pouring the concrete. The characteristic strength of concrete is defined as the compressive strength of a sample that has been aged for 28 days. Neither waiting for 28 days for such a test would serve the rapidity of construction, nor would neglecting it serve the quality control process on concrete in large construction sites. Therefore, rapid and reliable prediction of the strength of concrete would be of great significance. On this backdrop, the method is proposed to establish a predictive relationship between properties and proportions of ingredients of concrete, compaction factor, weight of concrete cubes and strength of concrete whereby the strength of concrete can be predicted at early age. Multiple regression analysis was carried out for predicting the compressive strength of concrete containing Portland Pozolana cement using statistical analysis for the concrete data obtained from the experimental work done in this study. The multiple linear regression models yielded fairly good correlation coefficient for the prediction of compressive strength for 7, 28 and 40 days curing. The results indicate that the proposed regression models are effectively capable of evaluating the compressive strength of the concrete containing Portaland Pozolana Cement. The derived formulas are very simple, straightforward and provide an effective analysis tool accessible to practicing engineers.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.5
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• pp.297-304
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• 2020

In this study, the prediction method was reviewed to process a cylindrical plate forming using machine learning as a data-driven approach by roll bending equipment. The calculation of the forming variables was based on the analysis using the mechanical relationship between the material properties and the roll bending machine in the bending process. Then, by applying the finite element analysis method, the accuracy of the deformation prediction model was reviewed, and a large number data set was created to apply to machine learning using the finite element analysis model for deformation prediction. As a result of the application of the machine learning model, it was confirmed that the calculation is slightly higher than the linear regression method. Applicable results were confirmed through the machine learning method.

• International Journal of Highway Engineering
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• v.19 no.1
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• pp.37-44
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• 2017

PURPOSES : This study is primarily focused on evaluating the effects of the non-linear stress-strain behavior of RAP concrete on structural response characteristics as is applicable to concrete pavement. METHODS : A 3D FE model was developed by incorporating the actual stress-strain behavior of RAP concrete obtained via flexural strength testing as a material property model to evaluate the effects of the non-linear stress-strain behavior to failure on the maximum stresses in the concrete slab and potential performance prediction results. In addition, a typical linear elastic model was employed to analyze the structural responses for comparison purposes. The analytical results from the FE model incorporating the actual stress-strain behavior of RAP concrete were compared to the corresponding results from the linear elastic FE model. RESULTS : The results indicate that the linear elastic model tends to yield higher predicted maximum stresses in the concrete as compared to those obtained via the actual stress-strain model. Consequently, these higher predicted stresses lead to a difference in potential performance of the concrete pavement containing RAP. CONCLUSIONS : Analysis of the concrete pavement containing RAP demonstrated that an appropriate analytical model using the actual stress-strain characteristics should be employed to calculate the structural responses of RAP concrete pavement instead of simply assuming the concrete to be a linear elastic material.

• Journal of Ocean Engineering and Technology
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• v.28 no.5
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• pp.396-403
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• 2014

This paper considered the prediction of the tension force in the design of a TLP tendon, particularly focusing on the springing problem. Springing is an important parameter that exerts a large tension in special cases. It is a nonlinear phenomenon and requires the 2nd-order wave loads to solve. In this paper, a new prediction method for springing and the resultant extreme tension on the tendon of a TLP is introduced. Using the 2nd-order response function computed using the commercial program WADAM, the probability density function of the 2nd-order tension is obtained from an eigenvalue analysis using a quadratic transfer function and sea spectra. A new method is then suggested to predict the extreme tension loads with respect to the number of occurrences. It is shown that the PDF suggested in this study properly predicts the extreme tension in comparison with the time histories of the 2nd-order tension. The expected tension force is larger than that from a linear analysis in the same time windows. This supports the use of the present method to predict the tension due to springing.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.13-17
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• 2019

Currently, the sports market continues to grow every year, and among them, professional baseball's entry income is larger than the rest of the professional league. In sports, strategies are used differently in different situations, and the analysis is based on data to decide which direction to implement. There is a part that a person misses in an analysis, and there is a possibility of a false analysis by subjective judgment. So, if this data analysis is done through artificial intelligence, the objective analysis is possible, and the strategy can be more rationalized, which helps to win the game. The most popular baseball to be applied to artificial intelligence to analyze athletes' strengths and weaknesses and then efficiently establish strategies to ease the competition. The data applied to the experiment were provided on the KBO official website, and the algorithms for forecasting applied linear regression. The results showed that the accuracy was 87%, and the standard error was ±5. Although the results of the experiment were not enough data, it would be possible to effectively use baseball strategies and predict the results of the game if the amount of data and regular data can be applied in the future.