• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.9 s.180
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• pp.2283-2291
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• 2000

The neural network method is applied to automatically measure the crack opening load under random loading. The crack opening results obtained are compared with the visual measured results. Fatigue crack growth under random loading is predicted using the crack opening data measured by the neural network method, and the prediction results are compared with experimental ones. It is found that the neural network method can be successfully applied to consistently measure the crack opening load under random loading and also gives some results different from the results by visual measurement.

• Journal of KSNVE
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• v.6 no.6
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• pp.735-747
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• 1996

In this paper, buckling reliability analyses of stiffened cylinder with random initial geometric imperfection under axial impact load are performed by the combined response surface method. The effect of random geometric imperfection on the failure probability and reliability is recognized quantitatively. Buckling reliability decreases with the increase of mean value, cov of initial geometric imperfection under the same external load. Buckling probability under impact load is greater than those under static load with the same condition. From the probabilistic characteristics of imapct buckling load, relation between reliability index and safety parameter can be obtained in addition to the relation between load and reliability index. And those results can be used to determine the range of required safety parameter and acceptable imperfaction.

• Industrial Engineering and Management Systems
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• v.15 no.2
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• pp.148-155
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• 2016

Existing power plants may consume significant amounts of fuel and require high operating costs, partly because of poor electrical power output estimates. This paper suggests a continuous conditional random field (C-CRF) model to predict more precisely the full-load electrical power output of a base load operated combined cycle power plant. We introduce three feature functions to model association potential and one feature function to model interaction potential. Together, these functions compose the C-CRF model, and the model is transformed into a multivariate Gaussian distribution with which the operation parameters can be modeled more efficiently. The performance of our model in estimating power output was evaluated by means of a real dataset and our model outperformed existing methods. Moreover, our model can be used to estimate confidence intervals of the predicted output and calculate several probabilities.

• Proceedings of the Korea Concrete Institute Conference
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• 1993.10a
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• pp.179-183
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• 1993

Loads acting on concrete structures are completely random in nature with respect to frequency, magnitude and order of loading, and are essentially distinct from the loads in two-stage and variable load fatigue test. Thus, this study proposes the fatigue test method generating random loads based on the analyzed result.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.6
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• pp.533-541
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• 2020

This study using ABAQUS investigates the nonlinear vibration responses when thermal and random acoustic loads are applied simultaneously to the stiffened composite panels. The nonlinear vibration analyses are performed with changing the number of stiffeners, and layup condition of the skin panel. The panel and stiffeners both are modeled using shell elements. Thermal load (ΔT) is assumed to have the temperature gradient through the thickness direction of the stiffened composite panel. The random acoustic load is represented as stationary white-Gaussian random pressure with zero mean and uniform magnitude over the panels. The thermal postbuckling analysis is conducted using RIKS method, and the nonlinear dynamic analysis is performed using Hilber-HughesTaylor time integration method. When ΔT = 25.18 ℃ and SPL = 105 dB are applied to the stiffened composite panel, the effect of the number of stiffener is investigated, and the snap-through responses are observed for composite panels without stiffeners and with 1 and 3 stiffeners. For investigation of the effect of layup condition of the skin panel, when ΔT = 38.53 ℃ and SPL = 110 dB are applied to the stiffened composite panel, the snap-through responses are shown when the fiber angle of the skin panel is 0°, 30°, and 60°.

• Structural Engineering and Mechanics
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• v.3 no.1
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• pp.1-10
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• 1995

This study presents a methodology for the system reliability analysis of cracked structures with random material properties, which are modeled as random fields, and crack geometry under random static loads. The finite element method provides the computational framework to obtain the stress intensity solutions, and the first-order reliability method provides the basis for modeling and analysis of uncertainties. The ultimate structural system reliability is effectively evaluated by the stable configuration approach. Numerical examples are given for the case of random fracture toughness and load.

• Journal of Electrical Engineering and information Science
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• v.2 no.5
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• pp.33-39
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• 1997

In this paper we proposed a new traffic load shedding scheme which maximizes the throughput of traffic control by decreasing the load of the hot-spot cell using minimum load cell selection (MLCS) algorithm and deployed control flow of calls to define characteristic for hadoff region. we compared the performance of the random shedding approach with that of the proposed algorithm. The results of simulation show that MLCS algorithm minimizes the cal blocking rate under a high-density traffic compared to the random shedding scheme.

• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Journal of Korean Society for Quality Management
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• v.20 no.2
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• pp.21-32
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• 1992

The only property of the random stresses to affect the life length are their damage amount and are independent of the especial realization in stress case. This paper shows the result is discussed that the randomness of the life length is caused by the randomness of the strength rather than by the randomness of the stress when the load is a random function and the strength is random as well. This special model is well-suited model for comparative calculations since it connects fatigue life for random stresses to fatigue life for periodically curve loads, which is usually measured in experiments.

• Structural Engineering and Mechanics
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• v.82 no.1
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• pp.31-40
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• 2022

In structural engineering, the material properties of the structures such as elastic modulus, shear modulus, density, and size may not be deterministic and may vary at different locations. The dynamic response analysis of such structures may need to consider these properties as stochastic. This paper introduces a stochastic finite element method (SFEM) approach to analyze moving loads problems. Firstly, Karhunen-Loéve expansion (KLE) is applied for expressing the stochastic field of material properties. Then the mathematical expression of the random field is substituted into the finite element model to formulate the corresponding random matrix. Finally, the statistical moment of the dynamic response is calculated by the point estimation method (PEM). The accuracy and efficiency of the dynamic response obtained from the KLE-PEM are demonstrated by the example of a moving load passing through a simply supported Euler-Bernoulli beam, in which the material properties (including elastic modulus and density) are considered as random fields. The results from the KLE-PEM are compared with those from the Monte Carlo simulation. The results demonstrate that the proposed method of KLE-PEM has high accuracy and efficiency. By using the proposed SFEM, the random vertical deflection of a high-speed railway (HSR) bridge is analyzed by considering the random fields of material properties under the moving load of a train.