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

• Nuclear Engineering and Technology
• /
• v.46 no.5
• /
• pp.619-632
• /
• 2014

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.4
• /
• pp.194-202
• /
• 2019

Reliability analysis of the components frequently starts with the data that manufacturer provides. If enough failure data are collected from the field operations, the reliability should be recomputed and updated on the basis of the field failure data. However, when the failure time record for a component contains only a few observations, all statistical methodologies are limited. In this case, where the failure records for multiple number of identical components are available, a valid alternative is combining all the data from each component into one data set with enough sample size and utilizing the useful information in the censored data. The ROK Navy has been operating multiple Patrol Killer Guided missiles (PKGs) for several years. The Korea Multi-Function Control Console (KMFCC) is one of key components in PKG combat system. The maintenance record for the KMFCC contains less than ten failure observations and a censored datum. This paper proposes a Bayesian approach with a Dirichlet mixture model to estimate failure time density for KMFCC. Trends test for each component record indicated that null hypothesis, that failure occurrence is renewal process, is not rejected. Since the KMFCCs have been functioning under different operating environment, the failure time distribution may be a composition of a number of unknown distributions, i.e. a mixture distribution, rather than a single distribution. The Dirichlet mixture model was coded as probabilistic programming in Python using PyMC3. Then Markov Chain Monte Carlo (MCMC) sampling technique employed in PyMC3 probabilistically estimated the parameters' posterior distribution through the Dirichlet mixture model. The simulation results revealed that the mixture models provide superior fits to the combined data set over single models.

• The Korean Journal of Applied Statistics
• /
• v.32 no.5
• /
• pp.763-782
• /
• 2019

This paper proposes a probabilistic document ranking model incorporating term dependencies. Document ranking is a fundamental information retrieval task. The task is to sort documents in a collection according to the relevance to the user query (Qin et al., Information Retrieval Journal, 13, 346-374, 2010). A probabilistic model is a model for computing the conditional probability of the relevance of each document given query. Most of the widely used models assume the term independence because it is challenging to compute the joint probabilities of multiple terms. Words in natural language texts are obviously highly correlated. In this paper, we assume a multinomial distribution model to calculate the relevance probability of a document by considering the dependency structure of words, and propose an information retrieval model to rank a document by estimating the probability with the maximum entropy method. The results of the ranking simulation experiment in various multinomial situations show better retrieval results than a model that assumes the independence of words. The results of document ranking experiments using real-world datasets LETOR OHSUMED also show better retrieval results.

• Journal of Korean Society of Water and Wastewater
• /
• v.24 no.2
• /
• pp.183-193
• /
• 2010

Well planned rehabilitation order of pipes is essential for efficient maintenance and management of Water Distribution Systems. In this study, not only deterioration rate of pipes but also structural and nonstructural failure which causes abnormal condition of WDS is considered to determine rehabilitation order. Probabilistic Neural Network is used for calculating deterioration rate at present and the importance of pipes is computed under structural and nonstructural failure by using Pipe by Pipe Failure Analysis and Effect Index. Utopian Approach, one of the Multi-Criteria Decision Making methods, is used for assessment of final rehabilitation order based on distance measure between utopian point and alternative one. Developed model in this study shows that it gives more reliable results than existing methods considering hydraulic relative importance does in application to real networks. In this point, the newly developed model, which gives advantages over existing models, can make a credible decision and simple application.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.6
• /
• pp.1294-1301
• /
• 2014

In this paper, separate probabilistic distribution models for voiced and unvoiced speech are estimated and utilized to improve speaker recognition performance. Also, in addition to the conventional mel-frequency cepstral coefficient, skewness, kurtosis, and harmonic-to-noise ratio are extracted and used for voiced speech intervals. Two kinds of scores for voiced and unvoiced speech are linearly fused with the optimal weight found by exhaustive search. The performance of the proposed speaker recognizer is compared with that of the conventional recognizer which uses mel-frequency cepstral coefficient and a unified probabilistic distribution function based on the Gassian mixture model. Experimental results show that the lower the number of Gaussian mixture, the greater the performance improvement by the proposed algorithm.

• Journal of the Korea Concrete Institute
• /
• v.21 no.6
• /
• pp.789-796
• /
• 2009

The randomness in the strength of a RC member is caused mainly by the variability of the mechanical properties of concrete and steel, the dimensions of concrete cross sections, and the placement of reinforcing bars and so on . Among those variations, the randomness and uncertainty of mechanical properties of concrete, such as compressive strength, tensile strength, and elastic modulus give the most significant influences and show relatively large statistical variations. In Korea, there has been little effort for the construction of its own statistical models for mechanical properties of concrete and steel, thus the foreign data have been utilized till now. In this paper, variability of compressive strength, tensile strength and elastic modulus of normal-weight structural concrete with various specified design compressive strength levels are examined based on the data obtained from a number of published and unpublished sources in this country and additional laboratory tests done by the authors. The inherent probabilistic models for compressive and tensile strength of normal-weight concrete are proposed as Gaussian distribution. Also, the relationships between compressive and splitting tensile strength and between compressive strength and elastic modulus in current KCI Code are verified and new ones are suggested based on local data.

• Publications of The Korean Astronomical Society
• /
• v.32 no.1
• /
• pp.205-208
• /
• 2017

Modelling and fitting the spectral energy distribution (SED) of galaxies or regions of galaxies is one of the most useful methods available to the astronomer nowadays. By modelling the SEDs and comparing the models to the observations, we can collect important information on the physical processes at play in the formation and evolution of galaxies. The models allow to follow the evolution of the galaxies from their formation on. The versatility of code is crucial because of the diversity of galaxies. The analysis is only relevant and useful if the models can correctly reproduce this diversity now and across (as best as possible) all redshifts. On the other hand, the code needs to run fast to compare several million or tens of millions of models and to select the best (on a probabilistic basis) one that best resembles the observations. With this important point in mind, it seems logical that we should efficiently make use of the computer power available to the average astronomer. For instance, it seems difficult, today, to model and fit SEDs without a parallelized code. We present the new Python version of CIGALE SED fitting code and its characteristics. CIGALE comes in two main flavours: CIGALE Classic to fit SEDs and CIGALE Model to create spectra and SEDs of galaxies at all redshifts. The latest can potentially be used in conjunction with galaxy evolution models of galaxy formation and evolution such as semi-analytic ones.

• Computers and Concrete
• /
• v.26 no.4
• /
• pp.351-365
• /
• 2020

This work presents numerical and experimental analyses of the behavior of reinforced-concrete deep beams with unconventional geometries. The main goal here is to experimentally and numerically study these geometries to find possible new behaviors due to the material nonlinearity of reinforced concrete with complex geometries. Usually, unconventional geometries result from innovative designs; in general, studies of reinforced concrete structures are performed only on conventional members such as beams, columns, and labs. To achieve the goal, four reinforced-concrete deep beams with geometries not addressed in the literature were tested. The models were numerically analyzed with the Adaptive Micro Truss Model (AMTM), which is the proposed method, to address new geometries. This work also studied the main parameters of the constitutive model of concrete based on a statistical analysis of the finite element (FE) results. To estimate the ultimate loads, FE simulations were performed using the Monte Carlo method. Based on the obtained ultimate loads, a probabilistic distribution was created, and the final ultimate loads were computed.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1991.10a
• /
• pp.29-32
• /
• 1991

It is well known that the fatigue damage process in composite materials is very complicated due to complex failure mechanisms that comprise debounding, matrix cracking, delamination and fiber splitting of laminates. Therefore, the residual strength, instead of a single dominant crack length, is chosen to describe the criticality of the damage accumulated in the sublaminate. In this study, two models for residual strength degradation established by Yang-Liu and Tanimoto-Ishikawa that are capable of predicting the statistical distribution of both fatigue life and residual strength have been investigated and compared. Statistical methodologies for fatigue life prediction of composite materials have frequently been adopted. However, these are usually based on a simplified probabilistic approach considering only the variation of fatigue test data. The main object of this work is to propose a fatigue reliability analysis model which accounts for the effect of all sources of variation such as fabrication and workmanship, error in the fatigue model, load itself, etc. The proposed model is examined using the previous experimental data of GFRP and it is shown that it can be practically applied for fatigue problems in composite materials.