• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.591-595
• /
• 2010

Conventional methods of model evaluation usually rely only on model performance based on a comparison of simulated variables to corresponding observations. However, this type of model evaluation has been criticized because of its insufficient consideration of the various uncertainty sources involved in modeling processes. This study aims to propose an extended model evaluation method using multiple assesment indices (MAIs) that consider not only the model performance but also the model structure and parameter uncertainties in rainfall-runoff modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250m, 500m, and 1km digital elevation models) were developed and assessed by three MAIs for model performance, model structural stability, and parameter identifiability. All the models provided acceptable performance in terms of a global response, but the simpler SFM and KWMSS1 could not accurately represent the local behaviors of hydrographs. In addition, SFM and KWMSS1 were structurally unstable; their performance was sensitive to the applied objective functions. On the other hand, the most sophisticated model, KWMSS2, performed well, satisfying both global and local behaviors. KMSS2 also showed good structural stability, reproducing hydrographs regardless of the applied objective functions; however, superior parameter identifiability was not guaranteed. Numerous parameter sets could lead to indistinguishable hydrographs. This result supports that while making a model complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty. The proposed model evaluation process can provide an effective guideline for identifying a reliable hydrologic model.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2008.04a
• /
• pp.602-607
• /
• 2008

A technique for reliability-based design optimization(RBDO) is developed based on the Bayesian approach, which can deal with the epistemic uncertainty arising due to the limited number of data. Until recently, the conventional RBDO was implemented mostly by assuming the uncertainty as aleatory which means the statistical properties are completely known. In practice, however, this is not the case due to the insufficient data for estimating the statistical information, which makes the existing RBDO methods less useful. In this study, a Bayesian reliability is introduced to take account of the epistemic uncertainty, which is defined as the lower confidence bound of the probability distribution of the original reliability. In this case, the Bayesian reliability requires double loop of the conventional reliability analyses, which can be computationally expensive. Kriging based dimension reduction method(KDRM), which is a new efficient tool for the reliability analysis, is employed to this end. The proposed method is illustrated using a couple of numerical examples.

• Structural Monitoring and Maintenance
• /
• v.5 no.3
• /
• pp.379-390
• /
• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.

• Structural Engineering and Mechanics
• /
• v.53 no.6
• /
• pp.1127-1141
• /
• 2015

The optimum design of liquid column dampers in seismic vibration control considering system parameter uncertainty is usually performed by minimizing the unconditional response of a structure without any consideration to the variation of damper performance due to uncertainty. However, the system so designed may be sensitive to the variations of input system parameters due to uncertainty. The present study is concerned with robust design optimization (RDO) of liquid column vibration absorber (LCVA) considering random system parameters characterizing the primary structure and ground motion model. The RDO is obtained by minimizing the weighted sum of the mean value of the root mean square displacement of the primary structure as well as its standard deviation. A numerical study elucidates the importance of the RDO procedure for design of LCVA system by comparing the RDO results with the results obtained by the conventional stochastic structural optimization procedure and the unconditional response based optimization.

• Structural Engineering and Mechanics
• /
• v.78 no.5
• /
• pp.529-543
• /
• 2021

Inevitable source-uncertainties in geometry configuration, boundary condition, and material properties may deviate the structural dynamics from its expected responses. This paper aims to examine the influence of these uncertainties on the vibration of functionally graded beams. Finite element procedures are presented for Timoshenko beams and utilized to generate reliable datasets. A prerequisite to the uncertainty quantification of the beam vibration using Monte Carlo simulation is generating large datasets, that require executing the numerical procedure many times leading to high computational cost. Utilizing artificial neural networks to model beam vibration can be a good approach. Initially, the optimal network for each beam configuration can be determined based on numerical performance and probabilistic criteria. Instead of executing thousands of times of the finite element procedure in stochastic analysis, these optimal networks serve as good alternatives to which the convergence of the Monte Carlo simulation, and the sensitivity and probabilistic vibration characteristics of each beam exposed to randomness are investigated. The simple procedure presented here is efficient to quantify the uncertainty of different stochastic behaviors of composite structures.

• Earthquakes and Structures
• /
• v.22 no.2
• /
• pp.157-168
• /
• 2022

A moment-independent importance measure analysis approach was introduced to quantify the effects of structural uncertainty parameters on probabilistic seismic demands of simply supported girder bridges. Based on the probability distributions of main uncertainty parameters in bridges, conditional and unconditional bridge samples were constructed with Monte-Carlo sampling and analyzed in the OpenSees platform with a series of real seismic ground motion records. Conditional and unconditional probability density functions were developed using kernel density estimation with the results of nonlinear time history analysis of the bridge samples. Moment-independent importance measures of these uncertainty parameters were derived by numerical integrations with the conditional and unconditional probability density functions, and the uncertainty parameters were ranked in descending order of their importance. Different from Tornado diagram approach, the impacts of uncertainty parameters on the whole probability distributions of bridge seismic demands and the interactions of uncertainty parameters were considered simultaneously in the importance measure analysis approach. Results show that the interaction of uncertainty parameters had significant impacts on the seismic demand of components, and in some cases, it changed the most significant parameters for piers, bearings and abutments.

• Industrial Engineering and Management Systems
• /
• v.7 no.3
• /
• pp.245-256
• /
• 2008

In order to cope with the ill-defined problem of human behavior being immanent uncertainty, several methodologies have been studied in game theoretic, social psychological and political science frameworks. As methods to arrange system elements systematically and draw out the consenting structural model concretively, ISM, FSM and DEMATEL based on graph theory etc. have been proposed. In this paper, we propose a modified structural modeling method to recognize the nature of problem. We introduce the statistical method to adjust the establishment levels in group decision situation. From this, it will become possible to obtain effectively and smoothly the structural model of group members in comparison with the traditional methods. Further we propose a procedure for achieving the consenting structural model of group members based on the structural modeling method. By applying the method to recognize the nature of ill-defined problems, it will be possible to solve the given problem effectively and rationally. In order to inspect the effectiveness of the method, we conduct a practical problem as an empirical study: "Behavior analysis of passengers for the Joban line of East Japan Railway Company after new railway service of Tsukuba Express opened".

• Journal of Korean Academy of Nursing
• /
• v.42 no.1
• /
• pp.105-115
• /
• 2012

Purpose: This study was done to propose a structural model to explain and predict psychosocial adjustment in patients with early breast cancer and to test the model. The model was based on the Stress-Coping Model of Lazarus and Folkman (1984). Methods: Data were collected from February 18 to March 18, 2009. For data analysis, 198 data sets were analyzed using SPSS/WIN12 and AMOS 7.0 version. Results: Social support, uncertainty, symptom experience, and coping had statistically significant direct, indirect and total effects on psychosocial adjustment, and optimism had significant indirect and total effects on psychosocial adjustment. These variables explained 57% of total variance of the psychosocial adjustment in patients with early breast cancer. Conclusion: The results of the study indicate a need to enhance psychosocial adjustment of patients with early breast cancer by providing detailed structured information and various symptom alleviation programs to reduce perceived stresses such as uncertainty and symptom experience. They also suggest the need to establish support systems through participation of medical personnel and families in such programs, and to apply interventions strengthening coping methods to give the patients positive and optimistic beliefs.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.10
• /
• pp.2651-2659
• /
• 2009

In this paper, inverse optimal control for nonlinear systems with structural uncertainty is considered. The first, the bounded of structural uncertainty is introduced and based on the control Lyapunov function, a theorem for the globally asymptotic stability is presented. From this a less conservative condition for the inverse optimal control is derived. The result is used to design an inverse optimal controller for a class of nonlinear systems, that improves and extends the existing results. The class of nonlinear system considered is also enlarger. The simulation results show the effectiveness of the method.

• 전자공학회논문지 IE
• /
• v.46 no.4
• /
• pp.49-56
• /
• 2009

In this paper, stabilization inverse optimal control for nonlinear systems with structural uncertainty is considered. Based on the control Lyapunov function, a theorem for the globally asymptotic stability is presented. From this a less conservative condition for the inverse optimal control is derived. The result is used to design an inverse optimal controller for a class of nonlinear systems, that improves and extends the existing results. The class of nonlinear system considered is also enlarger. The simulation results show the effectiveness of the method.