• Computational Structural Engineering
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• v.2 no.4
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• pp.89-98
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• 1989

In order to take account of the statistical properties of random variables used in the structural analysis, the conventional approach usually adopts the safety factor based on past experiences for the qualitative assessment of structural safety problem. Recently, new approach based on the probabilistic concept has been applied to the assessment of structural safety in order to circumvent the difficulties of the conventional approach in choosing the appropriate safety factor. Thus, computer program called "Probabilistic finite element method" is developed by incorporating the probabilistic concept into the conventional matrix method in order to investigate the effects of the random variables on the final output of the structural analysis. From the comparison of some examples, it can be concluded that the PFEM developed in this study deals consistently with the uncertainty of random variables and provides the rational tool for the assessment of structural safety of plane frame.

• Geomechanics and Engineering
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• v.10 no.6
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• pp.709-726
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• 2016

Corrugated-core sandwich panels are prevalent for many applications in industries. The researches performed with the aim of optimization of such structures in the literature have considered a deterministic approach. However, it is believed that deterministic optimum points may lead to high-risk designs instead of optimum ones. In this paper, an effort has been made to provide a reliable and robust design of corrugated-core sandwich structures through stochastic and probabilistic multi-objective optimization approach. The optimization is performed using a coupling between genetic algorithm (GA), Monte Carlo simulation (MCS) and finite element method (FEM). To this aim, Prob. Design module in ANSYS is employed and using a coupling between optimization codes in MATLAB and ANSYS, a connection has been made between numerical results and optimization process. Results in both cases of deterministic and probabilistic multi-objective optimizations are illustrated and compared together to gain a better understanding of the best sandwich panel design by taking into account reliability and robustness. Comparison of results with a similar deterministic optimization study demonstrated better reliability and robustness of optimum point of this study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1318-1326
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• 2008

This paper proposes an alternative methodology for deciding an optimum deterministic reliability level (IRR; Installed Reserve Rate) by using probabilistic reliability criterion (LOLE; Loss of Load Expectation). Additionally, case studies using the proposed method induce the characteristics of relationship between the probabilistic reliability index (LOLE) and deterministic reliability index (IRR) for 2008 and 2010 years in Korea power system. The case study presents a possibility that an optimum IRR level in Korea can be assessed using the proposed method. Korea power system has been using the LOLE criterion to determine the adequacy of installed capacity (ICAP) requirement. The criterion in Korea is that the loss of load expectation shall not exceed the available capacity more than five day in ten years (=0.5[days/year]), The probabilistic reliability evaluation and production cost simulation program which is called PRASim is used in order to evaluate the relationship and optimum IRR in this paper.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.63-72
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• 2021

Mobile crowd-sensing (MCS) is a promising sensing paradigm that leverages mobile users with smart devices to perform large-scale sensing tasks in order to provide services to specific applications in various domains. However, MCS sensing tasks may not always be successfully completed or timely completed for various reasons, such as accidentally leaving the tasks incomplete by the users, asynchronous transmission, or connection errors. This results in missing sensing data at specific locations and times, which can degrade the performance of the applications and lead to serious casualties. Therefore, in this paper, we propose a missing data inference approach, called missing data approximation with probabilistic tensor factorization (MDI-PTF), to approximate the missing values as closely as possible to the actual values while taking asynchronous data transmission time and different sensing locations of the mobile users into account. The proposed method first normalizes the data to limit the range of the possible values. Next, a probabilistic model of tensor factorization is formulated, and finally, the data are approximated using the gradient descent method. The performance of the proposed algorithm is verified by conducting simulations under various situations using different datasets.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.179-193
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• 2022

The shear behaviour of reinforced concrete members has been studied over the past decades by various researchers, and it can be simulated by analysing shear panel elements which has been regarded as a basic element of reinforced concrete members subjected to in-plane biaxial stresses. Despite various experimental studies on shear panel element which have been conducted so far, there are still a lot of uncertainties related to what influencing factors govern the shear behaviour and affect failure mechanism in reinforced concrete members. To identify the uncertainties, a finite element analysis can be used, which enables to investigate the impact of specific variables such as the reinforcement ratio, the shear retention factor, and the material characteristics including aggregate interlock, tension stiffening, compressive softening, and shear behaviour at the crack surface. In this study, a non-linear probabilistic analysis was conducted on reinforced concrete panels using a finite element method optimized for reinforced concrete members and advanced sampling techniques so that probabilistic analysis can be performed effectively. Consequently, this study figures out what analysis methodology and input parameters have the most influence on shear behaviour of reinforced concrete panels.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.988-992
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• 2011

The buckling characteristics of the continuous welded rail track(CWR) is uncertainly varied by many influence factors, such as rail temperature, operating conditions of a train and maintenance of the track etc. Therefore, applying the probabilistic approach method is essential to rationally consider uncertainty and randomness of the various parameters that affect the track buckling. In this study, the probabilistic approach analysis was carried out and the results were compared with the deterministic approach using the buckling probability evaluation system of CWR tracks developed by our research team. From the comparison, it was identified that a probabilistic approach can quantitatively assess the reliability of the CWR tracks based on failure probability and can be used as a tool for decision making in track design, maintenance and operating etc.

• International Journal of Concrete Structures and Materials
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• v.5 no.2
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• pp.97-111
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• 2011

Modeling creep of concrete has been one of the most challenging problems in concrete. Over the years, research has proven the significance of creep and its ability to influence structural behavior through loss of prestress, violation of serviceability limit states or stress redistribution. Because of this, interest in modeling and simulation of creep has grown significantly. A research program was planned to investigate the significance of different factors affecting creep of concrete. This research investigation is divided into two folds: first, an in-depth study of the RILEM creep database and development of a homogenous database that can be used for blind computational analysis. Second: developing a probabilistic Bayesian screening method that enables identifying the significance of the different factors affecting creep of concrete. The probabilistic analysis revealed a group of interacting parameters that seem to significantly influence creep of concrete.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.667-670
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• 2001

For major structural components periodic inspections and integrity assessments are needed for the safety. However, many flaws are undetectable because sampling inspection is carried out during in-service inspection. Probabilistic integrity assessment is applied to take into consideration of uncertainty and variance of input parameters arise due to material properties and undetectable cracks. This paper describes a Probabilistic Fracture Mechanics(PEM) analysis based on the Monte Carlo(MC) algorithms. Taking a number of sampling data of probabilistic variables such as fracture toughness value, crack depth and aspect ratio of an initial surface crack, a MC simulation of failure judgement of samples is performed. For the verification of this analysis, a comparison study of th PFM analysis using a commercial code, mathematical method is carried out and a good agreement was observed between those results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.12
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• pp.1055-1062
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• 2008

Engineering materials have been studied and developed remarkably for a long time. But, few reports about marine propeller materials are presented. Recently, some researchers have studied the material strength of marine propellers. However, studies on parametric sensitivity and probabilistic distribution of fatigue life of propeller materials have not been made yet. In this study, a method to predict the probabilistic distributions of fatigue life of propeller materials is presented, and the influence of several parameters on the life distribution is discussed.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.459-464
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• 2001

For major nuclear power plant components periodic inspections and integrity assessments are needed for the safety. But many flaws are undetectable due to sampling inspection. Probabilistic integrity assessment is applied to take into consideration of uncertainty and variance of input parameters arise due to material properties, applied load and undetectable flaws. This paper describes a Probabilistic Fracture Mechanics(PFM) analysis based on Monte Carlo(MC) algorithms. Taking important parameters as probabilistic variables such as fracture toughness, crack growth rate and flaw shape, failure probability of major nuclear power plant components is archived as a results of MC simulation. For the verification of these analysis, a comparison study of the PFM analysis using other commercial code, mathematical method is carried out and a good agreement was observed between those results.