• Smart Structures and Systems
• /
• v.10 no.4_5
• /
• pp.485-499
• /
• 2012

Mode shape expansion is useful in structural dynamic studies such as vibration based structural health monitoring; however most existing expansion methods can not consider the modelling errors in the finite element model and the measurement uncertainty in the modal properties identified from vibration data. This paper presents a reliable approach for expanding mode shapes with consideration of both the errors in analytical model and noise in measured modal data. The proposed approach takes the perturbed force as an unknown vector that contains the discrepancies in structural parameters between the analytical model and tested structure. A regularisation algorithm based on the Tikhonov solution incorporating the L-curve criterion is adopted to reduce the influence of measurement uncertainties and to produce smooth and optimised expansion estimates in the least squares sense. The Canton Tower benchmark problem established by the Hong Kong Polytechnic University is then utilised to demonstrate the applicability of the proposed expansion approach to the actual structure. The results from the benchmark problem studies show that the proposed approach can provide reliable predictions of mode shape expansion using only limited information on the operational modal data identified from the recorded ambient vibration measurements.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2006.04a
• /
• pp.275-282
• /
• 2006

System algorithms estimated by deterministic input may occur the error between predicted and actual output. Especially, actual system can't predict the exact outputs due to uncertainty and tolernce of input parameters. A single output to a set of inputs has a limited value without the variation. Hence, we should consider various scatters caused by the load assessment, material characteristics, stress analysis and manufacturing methods in order to perform the robust design or etimate the reliability of structure. The system design with uncertainty should perform the probabilistic structural optimization with the statistical response and the reliability. This method calculated the probability distributions of the characteristics such as stress by combining stress analysis, response surface methodology and Monte Carlo simulation and got the probabilistic sensitivity. The sensitivity of structural response with respect to in constant design variables was estimated by fracture probability. Therefore, this paper proposed the probabilistic reliability design method for fracture of uncorved freight end beam and the design criteria by fracture probability.

• Journal of the Korean GEO-environmental Society
• /
• v.16 no.5
• /
• pp.13-25
• /
• 2015

This paper proposes an extended model evaluation method that considers not only the model performance but also the model structure and parameter uncertainties in hydrologic modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250-m, 500-m, and 1-km digital elevation models) were developed and assessed by three evaluative criteria 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. Moreover, 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. A number of parameter sets could result in indistinguishable hydrographs. This result indicates that while making hydrologic models complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.23 no.6
• /
• pp.641-649
• /
• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• Smart Structures and Systems
• /
• v.15 no.3
• /
• pp.751-767
• /
• 2015

A new procedure is proposed for assessing probabilistic condition of structures considering effect of measured data uncertainty. In this procedure, multiple Finite Element (FE) models are identified by using weighting vectors that represent the uncertainty conditions of measured data. The distribution of structural parameters is analysed using a Principal Component Analysis (PCA) in relation to uncertainty conditions, and the identified models are classified into groups according to their similarity by using a K-means method. The condition of a structure is then assessed probabilistically using FE models in the classified groups, each of which represents specific uncertainty condition of measured data. Yeondae bridge, a steel-box girder expressway bridge in Korea, is used as an illustrative example. Probabilistic condition of the bridge is evaluated by the distribution of load rating factors obtained using multiple FE models. The numerical example shows that the proposed method can quantify uncertainty of measured data and subsequently evaluate efficiently the probabilistic condition of bridges.

• East Asian Economic Review
• /
• v.28 no.1
• /
• pp.95-140
• /
• 2024

This study empirically investigates the international transmission of China's uncertainty shocks. It estimates a time-varying parameter Bayesian global structural vector autoregressive model (TVP-BGVAR) using time series data for 33 countries to evaluate heterogeneous international linkage across countries and time. Uncertainty shocks are identified via sign restrictions. The empirical results reveal that an increase in uncertainty in China negatively affects the global economy, but those effects significantly vary over time. The effects of China's uncertainty shocks on the global economy have been significantly altered by China's WTO accession, the global financial crisis, and the recent US-China trade conflict. Furthermore, the effects of China's uncertainty shocks, typically on inflation, differ significantly across countries. Moreover, Trade openness appears crucial in explaining heterogeneous GDP responses across countries, whereas the international dimension of monetary policy appears to be important in explaining heterogeneous inflation responses across countries.

• Computational Structural Engineering
• /
• v.28 no.3
• /
• pp.13-17
• /
• 2015

본 기사에서는 모델링불확실성(modeling uncertainty)에 따른 신뢰성 해석결과의 가변성(variability)을 가능성 분포함수(possibility distribution function)를 구성하여 해결하는 방법을 AISC(1998), AIJ(1985), CSA(1994)에서 제안된 3개의 최대 D/t 계산식을 예로 들어 소개하였다. 확신정도가 측정된 신뢰성지수 들을 얻을 수 있으며, 확신정도를 고려한 신뢰성지수의 결정이 가능하게 된다. 다양한 형태의 불확실성에 대하여 그 형태에 맞는 적합한 불확실성 모델링을 사용하는 것도 중요하지만, 확률적 표현에 익숙한 우리의 인지구조를 고려하여 기존의 신뢰성 해석에 어떻게 다양한 불확실성 모델링 방법을 접목시킬 것인지에 대한 연구도 중요할 것이다.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.13 no.1
• /
• pp.16-22
• /
• 2014

Resonance refers to the magnification of a structural response which occurs when a linear lightly damped system is driven with a sinusoidal input at its natural frequency. An exploratory vibration test (a natural frequency measurement test) is very important for the vibration testing of machine parts, as the value measured in an actual laboratory affects test results. For this reason, it is necessary to estimate the measurement uncertainty to verify the reliability of this type of test. In this study, measurement uncertainty is estimated based on three uncertainty factors. The uncertain factors are the measured points in the machine parts, the resolution of the vibration equipment, and uncertainty of the calibration certificate.

• Earthquakes and Structures
• /
• v.2 no.3
• /
• pp.279-295
• /
• 2011

This paper develops a probabilistic methodology for the seismic reliability analysis of structures with random properties. The earthquake loading is assumed to be described in terms of response spectra. The proposed methodology takes advantage of the response spectra and thus does not require explicit dynamic analysis of the actual structure. Uncertainties in the structural properties (e.g. member cross-sections, modulus of elasticity, member strengths, mass and damping) as well as in the seismic load (due to uncertainty associated with the earthquake load specification) are considered. The structural reliability is estimated by determining the failure probability or the reliability index associated with a performance function that defines safe and unsafe domains. The structural failure is estimated using a performance function that evaluates whether the maximum displacement has been exceeded. Numerical illustrations of reliability analysis of elastic and elastic-plastic single-story frame structures are presented first. The extension of the proposed method to elastic multi-degree-of-freedom uncertain structures is also studied and a solved example is provided.

• Journal of Korean Academy of Nursing
• /
• v.45 no.1
• /
• pp.97-106
• /
• 2015

Purpose: This study was done to develop and test a structural model for caregiving experience including caregiving satisfaction and caregiving strain in families providing care for family members with a mental disorder. Methods: The Stress-appraisal-coping model was used as the conceptual framework and the structural equation model to confirm the path that explains what and how variables affect caregiving experience in these families. In this hypothesis model, exogenous variables were optimism, severity of illness and uncertainty. The endogenous variables were self efficacy, social support, caregiving satisfaction and caregiving strain. Data were collected using structured questionnaires. Results: Optimism and caregiving self-efficacy had significant direct and indirect effects on caregiving satisfaction. Optimism, severity of illness and uncertainty had significant direct and indirect effects on caregiving strain. The modified path model explained effects of optimism on caregiving self-efficacy with social support in the path structure as a mediator. Also, there were direct and indirect effects of optimism and uncertainty on caregiving satisfaction with social support and caregiving self-efficacy in the path structure as a mediators. Conclusion: Results suggest the need to improve caregiving self-efficacy of these families, establish support systems such as a mental health professional support programs for caregiving self-efficacy. Optimism, severity of illness and uncertainty perceived by families need to be considered in the development of support programs in order to increase their effectiveness.