• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.97-113
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• 2017

This paper proposes a method for sufficient dimension reduction (SDR) of mixture data. We consider mixture data containing more than one component that have distinct central subspaces. We adopt an approach of a model-based sliced inverse regression (MSIR) to the mixture data in a simple and intuitive manner. We employed mixture probabilistic principal component analysis (MPPCA) to estimate each central subspaces and cluster the data points. The results from simulation studies and a real data set show that our method is satisfactory to catch appropriate central spaces and is also robust regardless of the number of slices chosen. Discussions about root selection, estimation accuracy, and classification with initial value issues of MPPCA and its related simulation results are also provided.

• Earthquakes and Structures
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• v.25 no.6
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• International Journal of Highway Engineering
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• v.18 no.1
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• pp.1-11
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• 2016

PURPOSES : This study estimated the load effect of a single heavy truck to develop a live load model for the design and assessment of bridges located on an expressway with a limited truck entry weight. METHODS : The statistical estimation methods for the live load effect acting on a bridge by a heavy vehicle are reviewed, and applications using the actual measurement data for trucks traveling on an expressway are presented. The weight estimation of a single vehicle and its effect on a bridge are fundamental elements in the construction of a live load model. Two statistical estimation methods for the application of extrapolation in a probabilistic study and an additional estimation method that adopts the extreme value theory are reviewed. RESULTS : The proposed methods are applied to the traffic data measured on an expressway. All of the estimation methods yield similar results using the data measured when the weight limit has been relatively well observed because of the rigid enforcement of the weight regulation. On the other hand, when the estimations are made using overweight traffic data, the resulting values differ with the estimation method. CONCLUSIONS : The estimation methods based on the extreme distribution theory and the modified procedure presented in this paper can yield reasonable values for the maximum weight of a single truck, which can be applied in both the design and evaluation of a bridge on an expressway.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.349-359
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• 2017

During the past 25 years, in the context of probabilistic safety assessment, efforts have been directed towards establishment of comprehensive pipe failure event databases as a foundation for exploratory research to better understand how to effectively organize a piping reliability analysis task. The focused pipe failure database development efforts have progressed well with the development of piping reliability analysis frameworks that utilize the full body of service experience data, fracture mechanics analysis insights, expert elicitation results that are rolled into an integrated and risk-informed approach to the estimation of piping reliability parameters with full recognition of the embedded uncertainties. The discussion in this paper builds on a major collection of operating experience data (more than 11,000 pipe failure records) and the associated lessons learned from data analysis and data applications spanning three decades. The piping reliability analysis lessons learned have been obtained from the derivation of pipe leak and rupture frequencies for corrosion resistant piping in a raw water environment, loss-of-coolant-accident frequencies given degradation mitigation, high-energy pipe break analysis, moderate-energy pipe break analysis, and numerous plant-specific applications of a statistical piping reliability model framework. Conclusions are presented regarding the feasibility of determining and incorporating aging effects into probabilistic safety assessment models.

• Journal of Computing Science and Engineering
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• v.3 no.3
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• pp.143-164
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• 2009

This article surveys recent research in the area of language modeling (sometimes called statistical language modeling) approaches to information retrieval. Language modeling is a formal probabilistic retrieval framework with roots in speech recognition and natural language processing. The underlying assumption of language modeling is that human language generation is a random process; the goal is to model that process via a generative statistical model. In this article, we discuss current research in the application of language modeling to information retrieval, the role of semantics in the language modeling framework, cluster-based language models, use of language modeling for XML retrieval and future trends.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10b
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• pp.656-661
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• 1998

This paper presents a method of statistical analysis and sensitivity analysis of creep and shrinkage effects in PSC box girder bridges. The statistical and sensitivity analyses are performed by using the numerical simulation of Latin Hypercube sampling. For each sample, the time-dependent structural analysis is performed to produce response data, which are then statistically analyzed. The probabilistic prediction of the confidence limits on long-term effects of creep and shrinkage is then expressed. Three measures are examined to quantify the sensitivity of the outputs to each of the input variables. These are rank correlation coefficient(RCC), partial rank correlation coefficient(PRCC) and standardized rank regression coefficient(SRRC) computed on the ranks of the observations. Probability band widens with time, which indicates an increase of prediction uncertainty with time. The creep model uncertainty factor and the relative humidity appear as the most dominant factors with regard to the model output uncertainty.

• Wind and Structures
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• v.1 no.4
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• pp.287-302
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• 1998

This paper formulates a probabilistic model which is able to represent the maximum instantaneous wind velocity. Unlike the classical methods, where the randomness is circumscribed within the mean maximum component, this model relies also on the randomness of the maximum value of the turbulent fluctuation. The application of the FOSM method furnishes the first and second statistical moments in closed form. The comparison between the results herein obtained and those supplied by classical methods points out the central role of the turbulence intensity. Its importance is exalted when extending the analysis from the wind velocity to the wind pressure.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.1
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• pp.21-36
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• 2018

The incidence of collision damage models on oil tanker and bulk carrier reliability is investigated considering the IACS deterministic model against GOALDS/IMO database statistics for collision events, substantiating the probabilistic model. Statistical properties of hull girder residual strength are determined by Monte Carlo simulation, based on random generation of damage dimensions and a modified form of incremental-iterative method, to account for neutral axis rotation and equilibrium of horizontal bending moment, due to cross-section asymmetry after collision events. Reliability analysis is performed, to investigate the incidence of collision penetration depth and height statistical properties on hull girder sagging/hogging failure probabilities. Besides, the incidence of corrosion on hull girder residual strength and reliability is also discussed, focussing on gross, hull girder net and local net scantlings, respectively. The ISSC double hull oil tanker and single side bulk carrier, assumed as test cases in the ISSC 2012 report, are taken as reference ships.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.429-430
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• 2009

The flexural strength of reinforced concrete members is strongly governed by mechanical properties of reinforcing bars, especially by yield strength, which have many uncertainties. The correct choice of probabilistic models for yield strength of reinforcement is an essential step to assure the safety and reliability of members. In this paper, a probabilistic model of yield strength of reinforcing bars is proposed based on literature and own experimental data.

• Wind and Structures
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• v.2 no.2
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• pp.127-131
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• 1999

This paper describes probabilistic characteristics of damping in a tall building based on the results of full-scale measurement. It is found, through statistical analysis of the damping data, that the probability density function(PDF) of damping at the high amplitude plateau can be well represented by Normal distribution (Gaussian distribution). A stochastic damping model is proposed to estimate amplitude-dependent damping for practical application.