• International conference on construction engineering and project management
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• 2011.02a
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• pp.534-541
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• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.5
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• pp.267-274
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• 2024

This study aims to propose a simplified equation for estimating the bond strength of corroded reinforcing bars. To this end, extensive parametric analyses were performed using the detailed analysis method presented in the authors' previous study, where a wide range of critical variables were considered, such as compressive strength of concrete, net cover thickness, and reinforcing bar diameter. The sensitivity in bond strength of the corroded reinforcing bar according to each variable was evaluated. On this basis, a simplified formula for the bond strength of the corroded reinforcing bar was derived through regression analysis. The proposed equation was rigorously tested and verified using the bond test results of corroded reinforcing bars collected from the literature. The results confirmed that the proposed equation could estimate the bond strengths of specimens with better accuracy than the existing models, providing a reliable tool for engineers and researchers. In addition, the proposed equation was used to analyze the development length required for corroded tensile reinforcement to exert its yield strength, and it showed that the cover thickness of concrete must be at least four times the diameter of the reinforcing bar to achieve the yielding strength of reinforcing bar even at a corrosion degree of more than 5.0%.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.369-379
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• 2014

The Statistical regression model has been used to construct crash prediction models, despite its limitations in assuming data distribution and functional form. In response to the limitations associated with the statistical regression models, a few studies based on non-parametric methods such as neural networks have been proposed to develop crash prediction models. However, these models have a major limitation in that they work as black boxes, and therefore cannot be directly used to identify the relationships between crash frequency and crash factors. A genetic programming model can find a solution to a problem without any specified assumptions and remove the black box effect. Hence, this paper investigates the application of the genetic programming technique to develope the crash prediction model. The data collected from the Gyeongbu expressway during the past three years (2010-2012), were separated into straight and curve sections. The random forest technique was applied to select the important variables that affect crash occurrence. The genetic programming model was developed based on the variables that were selected by the random forest. To test the goodness of fit of the genetic programming model, the RMSE of each model was compared to that of the negative binomial regression model. The test results indicate that the goodness of fit of the genetic programming models is superior to that of the negative binomial models.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.965-976
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• 2015

Determining relevant variables for a regression model is important in regression analysis. Recently, a variable selection methods using a penalized likelihood with various penalty functions (e.g. LASSO and SCAD) have been widely studied in simple statistical models such as linear models and generalized linear models. The advantage of these methods is that they select important variables and estimate regression coefficients, simultaneously; therefore, they delete insignificant variables by estimating their coefficients as zero. We study how to select proper variables based on penalized hierarchical likelihood (HL) in semi-parametric frailty models that allow three penalty functions, LASSO, SCAD and HL. For the variable selection we develop a new function in the "frailtyHL" R package. Our methods are illustrated with breast cancer survival data from the Medical Center at Chonnam National University in Korea. We compare the results from three variable-selection methods and discuss advantages and disadvantages.

• Geotechnical Engineering
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• v.13 no.6
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• pp.107-122
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• 1997

This paper presents a ground surface settlement prediction method for shield tunneling in cohesive soils. In order to develop the method, a parametric study on shield tunneling was performed by using a threetimensional elasto-plastic finite element analysis, which can simulate the construction procedure. By using the results of the finite element analysis, the ground movement mechanism was investigated and a base which relates the ground surface settlement and iuluencing factors was formed. The data base was then used to formulate semi -empirical equations for both surface settlement ratio above tunnel face and imflection point by means of a regression analysis. Furthermore, a prediction method for transverse and longitudinal surface settlement profiles was suggested by using the leveloped equations in conjunction with the normal probability curve. Effectiveness of the developed method was illustrated by comparing settlement profiles obtained by using the developed method with the results of finite element analysis and measured data. Based on the comparison, it was concluded that the developed method can be effectively rosed for practical applications at least within the conditions investigated.

• Journal of the Korean GEO-environmental Society
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• v.7 no.1
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• pp.5-18
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• 2006

Stone column or granular compaction pile have been used in widely during the several decades as a technique to reinforce soft cohesive soils and increase bearing capacity, accelerate consolidation settlement of the foundation soil. The bearing capacity of the granular compaction pile is governed mainly by the lateral confining pressure mobilized in the native soft soil to restrain bulging collapse of the granular pile. Therefore, the technique becomes unfeasible in soft, compressible clayey soils that do not provide sufficient lateral confinement. This paper presents the main results of numerical study of granular compaction pile which is partly mixed with lean concrete. 3D finite element analyses are performed with composite reinforced foundations by both granular compaction pile and partly mixed granular compaction pile with lean-mixed concrete. Finally, a regression formula for calculating settlement reduction coefficients is proposed in this study by using numerical analysis results and applicability of the proposed method is identified by a series of parametric study about settlement reduction coefficients.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.41-65
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• 2018

In Korea, as the mortality rate improves in a shorter period of time than in developed countries, it is important to consider the selection of the time series as well as the model selection in the mortality projection. Therefore, this study proposed a method using the multiple regression model in respect to the selection of the time series period. In addition, we investigate the problems that arise when various time series are used based on the Lee-Carter (LC) model, the kinds of LC model along with Lee-Miller (LM) and Booth-Maindonald-Smith (BMS), and the non-parametric model such as functional data model (FDM) and Coherent FDM, and examine differences in the age-specific mortality rate and life expectancy projection. Based on the analysis results, the age-specific mortality rate and predicted life expectancy of men and women are calculated for the year 2030 for each model. We also compare the mortality rate and life expectancy of the next generation provided by Korean Statistical Information Service (KOSIS).

• Journal of Korean Society of Steel Construction
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• v.13 no.3
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• pp.289-299
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• 2001

The purpose of this study was to analyze the characteristics of semi-igid behavior of the steel tubular column to H-beam connection reinforced with exterior square-plate diaphragms and to check the main parameters that affect this behavior. Steel tube connections without interior diaphragm and/or complicated exterior diaphragm show the considerable flexibility due to out of-plane deformation of tube flange. For the exact analysis well-reflected the effect of this flexibility on the overall frame performance. it need to find out the moment-rotation curve function that well trace the result of experiment in the whole region and the function should be simply transformed into an adequate form for the nonlinear analysis program. After collecting several test data same to the connection type considered. we carried out FEM analysis using ANSYS for the assumed beam-to-column connection developed from the simple tension test and the results are compared with experimental values. Based on the parametric study. we proposed the moment-relation curve function and performed the multiple-regression analysis procedure for three parameters consisting of this function with the main geometric parameter of this connection type.

• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.460-472
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• 2015

The aim of this study is to analyze the Weekend and January effect in the Ghana Stock Exchange (GSE) using daily closing prices of GSE-All Share Index (ASI) and Composite Index (CI) between the period of January 4th, 2005 and December 31st, 2013. The dataset covers the period of 2005 to 2010 (6 years) for the ASI and 2011 to 2013 (3 years) for the CI. The following results are obtained based on a parametric regression using dummy variables. First, no weekly effect or anomaly is documented for both GSE-ASI and GSE-CI. Second, market abnormalities are captured for both GSE-ASI and GSE-CI over their respective entire periods. However, no consistent April effect is found for ASI when the period was segregated into two periods of three years. The April effect is uncovered for the GSE-ASI at 5% significant level while the January effect is found for the GSE-CI at 1% significant level.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1429-1435
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• 2020

Applying an accurate parametric prediction model to identify abnormal or false pressurizer water levels (PWLs) is critical to the safe operation of marine pressurized water reactors (PWRs). Recently, deep-learning-based models have proved to be a powerful feature extractor to perform high-accuracy prediction. However, the effectiveness of models still suffers from two issues in PWL prediction: the correlations shifting over time between PWL and other feature parameters, and the example imbalance between fluctuation examples (minority) and stable examples (majority). To address these problems, we propose a cost-sensitive mechanism to facilitate the model to learn the feature representation of later examples and fluctuation examples. By weighting the standard mean square error loss with a cost-sensitive factor, we develop a Cost-Sensitive Long Short-Term Memory (CSLSTM) model to predict the PWL of PWRs. The overall performance of the CSLSTM is assessed by a variety of evaluation metrics with the experimental data collected from a marine PWR simulator. The comparisons with the Long Short-Term Memory (LSTM) model and the Support Vector Regression (SVR) model demonstrate the effectiveness of the CSLSTM.