• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.207-224
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• 2022

The mechanical behavior of prestressed concrete haunched beams (PSHBs) was investigated in depth using a finite element modeling technique in this study. The efficiency of finite element modeling was investigated in the first stage by taking into account a previous study from the literature. The first stage's findings suggested that finite element modeling might be preferable for modeling PSHBs. In the second stage of the research, a comprehensive parametric study was carried out to determine the effect of each parameter on PSHB load capacity, including haunch angle, prestress level, compressive strength, tensile reinforcement ratio, and shear span to depth ratio. PSHBs and prestressed concrete rectangular beams (PSRBs) were also compared in terms of capacity. Stochastic analysis was used in the third stage to define the uncertainty in PSHB capacity by taking into account uncertainty in geometric and material parameters. Standard deviation, coefficient of variation, and the most appropriate probability density function (PDF) were proposed as a result of the analysis to define the randomness of capacity of PSHBs. In the study's final section, a new equation was proposed for using symbolic regression to predict the load capacity of PSHBs and PSRBs. The equation's statistical results show that it can be used to calculate the capacity of PSHBs and PSRBs.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1299-1306
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• 2011

In this study, crack-growth model parameters subjected to variable amplitude loading are estimated in the form of a probability distribution using the method of Bayesian parameter estimation. Huang's model is employed to describe the retardation and acceleration of the crack growth during the loadings. The Markov Chain Monte Carlo (MCMC) method is used to obtain samples of the parameters following the probability distribution. As the conventional MCMC method often fails to converge to the equilibrium distribution because of the increased complexity of the model under variable amplitude loading, an improved MCMC method is introduced to overcome this shortcoming, in which a marginal (PDF) is employed as a proposal density function. The model parameters are estimated on the basis of the data from several test specimens subjected to constant amplitude loading. The prediction is then made under variable amplitude loading for the same specimen, and validated by the ground-truth data using the estimated parameters.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.199-210
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• 1999

The number of car accident is Recently on the increase in Korea because of the explosive increase of cars, the poor road condition, the lack of safety facility, and others. The insurant with a accident has to decide whether receiving a insurance or not. In this paper, we represent a reasonable decision support material by calculating the approximate insurance fee based on the discount rate and premium additive rate, which is changed by the accident type and the accident expenditure. Practically, there is difference in the standard insurance rate and premium additive rate according to the accident type and the accident expenditure in Korea. The premium additive rate is assessed considering the number of accident, the pattern of accident, and the reason of accident for 3 years. In this paper, we represent a decision making method considering not only the first-time car accident but also the future car accident. For considering the repeated accident, we analyzed the real data accumulated until the year of 1996 from S Insurance Company, and estimated the probability density function between the first and the second-time accident, and executed the goodness of fit test using ARENA and STATISTICA software. Using this conditional PDF, we can calculate the insurance fee next 3 years and compare the insurance fee with the equivalent present value of cash flows. The program performing this analysis is represented, and written in VISUAL BASIC Language. We tried to suggest an accurate guideline for the insurant to decide the insurance coverage rationally, and tried to correct a wrong idea of dependence on the car insurance only by the amount of the accident expenditure. And we expect this study can generally be applied to many different accident types under the uncertain circumstances in our daily life.

• Wind and Structures
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• v.18 no.6
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• pp.693-715
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• 2014

Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.417-423
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• 2007

Oxy-gasification or oxygen-blown gasification, enables a clean and efficient use of coal and opens a promising way to CO2 capture. The coal gasification process of a slurry feed type, entrained-flow coal gasifier was numerically predicted in this paper. The purposes of this study are to develop an evaluation technique for design and performance optimization of coal gasifiers using a numerical simulation technique, and to confirm the validity of the model. By dividing the complicated coal gasification process into several simplified stages such as slurry evaporation, coal devolatilization, mixture fraction model and two-phase reactions coupled with turbulent flow and two-phase heat transfer, a comprehensive numerical model was constructed to simulate the coal gasification process. The influence of turbulence on the gas properties was taken into account by the PDF (Probability Density Function) model. A numerical simulation with the coal gasification model is performed on the Conoco-Philips type gasifier for IGCC plant. Gas temperature distribution and product gas composition are also presented. Numerical computations were performed to assess the effect of variation in oxygen to coal ratio and steam to coal ratio on reactive flow field. The concentration of major products, CO and H2 were calculated with varying oxygen to coal ratio (0.2-1.5) and steam to coal ratio(0.3-0.7). To verify the validity of predictions, predicted values of CO and H2 concentrations at the exit of the gasifier were compared with previous work of the same geometry and operating points. Predictions showed that the CO and H2 concentration increased gradually to its maximum value with increasing oxygen-coal and hydrogen-coal ratio and decreased. When the oxygen-coal ratio was between 0.8 and 1.2, and the steam-coal ratio was between 0.4 and 0.5, high values of CO and H2 were obtained. This study also deals with the comparison of CFD (Computational Flow Dynamics) and STATNJAN results which consider the objective gasifier as chemical equilibrium to know the effect of flow on objective gasifier compared to equilibrium. This study makes objective gasifier divided into a few ranges to study the evolution of the gasification locally. By this method, we can find that there are characteristics in the each scope divided.

• The Pure and Applied Mathematics
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• v.11 no.1
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• pp.97-102
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• 2004

In this paper we establish some recurrence relations satisfied by quotient moments of upper record values from the Pareto distribution. Let ｛$X_n,n\qeq1$｝be a sequence of independent and identically distributed random variables with a common continuous distribution function(cdf) F($chi$) and probability density function(pdf) f($chi$). Let $Y_n\;=\;mas{X_1,X_2,...,X_n}$ for $ngeq1$. We say $X_{j}$ is an upper record value of {$X_{n},n\geq1$}, if $Y_{j}$＞$Y_{j-1}$,j＞1. The indices at which the upper record values occur are given by the record times ${u( n)}n,\geq1$, where u(n) = min{j｜j ＞u(n-l), $X_{j}$＞$X_{u(n-1)}$,n\qeq2$ and u(l) = 1. Suppose $X{\epsilon}PAR(\frac{1}{\beta},\frac{1}{\beta}$ then E$(\frac{{X^\tau}}_{u(m)}}{{X^{s+1}}_{u(n)})\;=\;\frac{1}{s}E$ E$(\frac{{X^\tau}}_{u(m)}{{X^s}_{u(n-1)}})$ - $\frac{(1+\betas)}{s}E(\frac{{X^\tau}_{u(m)}}{{X^s}_{u(n)}}$ and E$(\frac{{X^{\tau+1}}_{u(m)}}{{X^s}_{u(n)}})$ = $\frac{1}{(r+1)\beta}$ [E$(\frac{{X^{\tau+1}}}_u(m)}{{X^s}_{u(n-1)}})$ - E$(\frac{{X^{\tau+1}}_u(m)}}{{X^s}_{u(n-1)}})$ - (r+1)E$(\frac{{X^\tau}_{u(m)}}{{X^s}_{u(n)}})$]

• Korean Journal of Mathematics
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• v.12 no.1
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• pp.15-22
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• 2004

In this paper we establish some recurrence relations satisfied by quotient moments of upper record values from the power distribution. Let {$X_n$, $n{\geq}1$} be a sequence of independent an identically distributed random variables with a common continuous distribution function(cdf) $F(x)$ and probability density function(pdf) $f(x)$. Let $Y_n=max\{X_1,X_2,{\cdots},X_n\}$ for $n{\geq}1$. We say $X_j$ is an upper record value of {$X_n$, $n{\geq}1$}, if $Y_j$ > $Y_{j-1}$, $j$ > 1. The indices at which the upper record values occur are given by the record times {$u(n)$}, $n{\geq}1$, where $u(n)=min\{j{\mid}j>u(n-1),X_j>X_{u(n-1)},n{\geq}2\}$ and $u(1)=1$. Suppose $X{\in}POW(0,1,{\theta})$ then $$E\left(\frac{X^r_{u(m)}}{X^{s+1}_{u(n)}}\right)=\frac{\theta}{s}E\left(\frac{X^r_{u(m)}}{X^s_{u(n-1)}}\right)+\frac{(s-\theta)}{s}E\left(\frac{X^r_{u(m)}}{X^s_{u(n)}\right)\;and\;E\left(\frac{X^{r+1}_{u(m)}}{X^s_{u(n)}}\right)=\frac{\theta}{n+1}\left[E\left(\frac{X^{r+1}_{u(m-1)}}{X^s_{u(n+1)}}\right)-E\left(\frac{X^{r+1}_{u(m)}}{X^s_{u(n-1)}}\right)+\frac{r+1}{\theta}E\left(\frac{X^r_{u(m)}}{X^s_{u(n)}}\right)\right]$$.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• Economic and Environmental Geology
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• v.29 no.6
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• pp.713-724
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• 1996

The time-series resident solute concentrations, monitored at two field plots using the automated 144-channel TDR system by Kim (this issue), are used to investigate the dominant transport mechanism at field scale. Two models, based on contradictory assumptions for describing the solute transport in the vadose zone, are fitted to the measured mean breakthrough curves (BTCs): the deterministic one-dimensional convection-dispersion model (CDE) and the stochastic-convective lognormal transfer function model (CLT). In addition, moment analysis has been performed using the probability density functions (pdfs) of the travel time of resident concentration. Results of moment analysis have shown that the first and second time moments of resident pdf are larger than those of flux pdf. Based on the time moments, expressed in function of model parameters, variance and dispersion of resident solute travel times are derived. The relationship between variance or dispersion of solute travel time and depth has been found to be identical for both the time-series flux and resident concentrations. Based on these relationships, the two models have been tested. However, due to the significant variations of transport properties across depth, the test has led to unreliable results. Consequently, the model performance has been evaluated based on predictability of the time-series resident BTCs at other depths after calibration at the first depth. The evaluation of model predictability has resulted in a clear conclusion that for both experimental sites the CLT model gives more accurate prediction than the CDE model. This suggests that solute transport at natural field soils is more likely governed by a stream tube model concept with correlated flow than a complete mixing model. Poor prediction of CDE model is attributed to the underestimation of solute spreading and thus resulting in an overprediction of peak concentration.

• Korean Journal of Clinical Pharmacy
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• v.21 no.2
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• pp.74-80
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• 2011

Population pharmacokinetics for gentamicin were compared with 20 Korean patients (14 male and 6 female) and 25 Caucasian appendicitis patients (16 male and 9 female). Two to six blood specimens were collected from all patients at the following times : just before a regularly scheduled infusion and at 0.5 hour after the end of a 0.5 hour infusion. Nonparametric expected maximum(NPEM) algorithm for population modeling was used. The estimated parameters were the elimination rate constant(K), the slope(KS) of the relationship between K versus creatinine clearance(Ccr), the apparent volume of distribution (V), the slope(VS) of the relationship between V versus weight, gentamicin clearance(CL) and the slope(CS) of the relationship between CL versus Ccr and the V. The output includes two marginal probability density function(PDF), means, medians, modes, variance, skewness, kurtosis, and CV%. The mean K(KS) were$0.402{\pm}0.129hr^{-1}$ ($0.00486{\pm}0.00197[hr{\cdot}mL/min/1.73m^2]^{-1}$) and $0.425{\pm}0.137hr^{-1}$($0.00432{\pm}0.00168[hr{\cdot}mL/min/1.73m^2]^{-1}$) for Korean and Caucasian populations, respectively. The mean V(VS) were not different at $14.3{\pm}3.69L$($0.241{\pm}0.0511L/kg$) and $15.8{\pm}4.81L$($0.236{\pm}0.0531L/kg$) for Korean and Caucasian populations, respectively (P>0.2). The mean CL(CS) were $5.68{\pm}1.69L/hr$ ($0.0714{\pm}0.0222L/kg[hr{\cdot}mL/min/1.73m^2]$) and $6.29{\pm}1.84L/hr$ ($0.0629{\pm}0.0189L/kg[hr{\cdot}mL/min/1.73m^2]$) for Korean and Caucasian populations, respectively. There are no differences in gentamicin pharmacokinetics between Korean and Caucasian appendicitis patients.