• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• Composites Research
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• v.19 no.3
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• pp.7-14
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• 2006

In this paper, an integrated probabilistic strength analysis was conducted to predict the reliability of a composite pressure vessel under inner pressure loading condition. As a probabilistic strength analysis, the probabilistic progressive failure model consisting of progressive failure model and Monte Carlo simulation was incorporated with a commercial FEA code, ABAQUS Standard, to perform the probabilistic failure analysis of composite structure which has a complex shape and boundary conditions. As design random variables, the laminar strengths of each direction were considered. Finally, from probabilistic strength analysis, the scattering of burst pressure could be explained and the reliability of composite pressure vessel could be obtained for each component. In case of composite structures in mass production, the effects of uncertainties in material and manufacturing on the performance of composite structures would apparently become larger. So, the probabilistic strength analysis is essential for the structural design of composite structures in mass production.

• Earthquakes and Structures
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• v.26 no.4
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• pp.311-326
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• 2024

Transmission tower structures are particularly susceptible to damage and even collapse under strong seismic ground motions. Conventional seismic analyses of transmission towers are usually performed by considering only ground motion uncertainty while ignoring structural uncertainty; consequently, the performance evaluation and failure prediction may be inaccurate. In this context, the present study numerically investigates the seismic responses and failure mechanism of transmission towers by considering multiple sources of uncertainty. To this end, an existing transmission tower is chosen, and the corresponding three-dimensional finite element model is created in ABAQUS software. Sensitivity analysis is carried out to identify the relative importance of the uncertain parameters in the seismic responses of transmission towers. The numerical results indicate that the impacts of the structural damping ratio, elastic modulus and yield strength on the seismic responses of the transmission tower are relatively large. Subsequently, a set of 20 uncertainty models are established based on random samples of various parameter combinations generated by the Latin hypercube sampling (LHS) method. An uncertainty analysis is performed for these uncertainty models to clarify the impacts of uncertain structural factors on the seismic responses and failure mechanism (ultimate bearing capacity and failure path). The numerical results show that structural uncertainty has a significant influence on the seismic responses and failure mechanism of transmission towers; different possible failure paths exist for the uncertainty models, whereas only one exists for the deterministic model, and the ultimate bearing capacity of transmission towers is more sensitive to the variation in material parameters than that in geometrical parameters. This research is expected to provide an in-depth understanding of the influence of structural uncertainty on the seismic demand assessment of transmission towers.

• Journal of the Korean earth science society
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• v.26 no.7
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• pp.668-673
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• 2005

To collect or transfer samples of gaseous pollutants, various types of tubing are used. Hence, to analyze the uncertainties associated with the use of tubings, a series of comparative test were designed and conducted using the RSC standards with different concentration ranges. For the purpose of this study, we prepared tubings made of six different types of material which include: [1] silco-steel (S1), [2] stainless steel (S2), [3] silicone (S3), [4] PTFE Teflon (T1), [5] tygon (T2), and [6] brass (B). The patterns of RSC loss on to tubing walls, when compared on the basis of the least reactive material S1, exhibited that the extent of RSC loss varied dynamically. It was found that Teflon is highly stable. However, other materials tend to exhibit contrasting patterns of loss. S2 and B show significant loss of light RSC $(H_2S\;and\;CH_3SH)$, while S3 and T2 experience notable loss of heavy RSC (DMS and DMDS).

• Structural Engineering and Mechanics
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• v.15 no.4
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• pp.381-394
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• 2003

While constructing multistorey buildings with reinforced concrete framed structures it is a common practice to provide parking space for vehicles at the ground floor level. This floor will generally consist of open frames without any infilled walls and is called an open-storey. From a post disaster damage survey carried out, it was noticed that during the January 26, 2001 Bhuj (Gujarat, India) earthquake, a large number of reinforced concrete framed buildings with open-storey at ground floor level, suffered extensive damage and in some cases catastrophic collapse. This has brought into sharp focus the need to carry out systematic studies on the seismic vulnerability of such buildings. Determination of vulnerability requires realistic structural response estimations taking into account the stochasticity in the loading and the system parameters. The stochastic finite element method can be effectively used to model the random fields while carrying out such studies. This paper presents the details of stochastic finite element analysis of a five-storey three-bay reinforced concrete framed structure with open-storey subjected to standard seismic excitation. In the present study, only the stochasticity in the system parameters is considered. The stochastic finite element method used for carrying out the analysis is based on perturbation technique. Each random field representing the stochastic geometry/material property is discretised into correlated random variables using spatial averaging technique. The uncertainties in geometry and material properties are modelled using the first two moments of the corresponding parameters. In evaluating the stochastic response, the cross-sectional area and Young' modulus are considered as independent random fields. To study the influence of correlation length of random fields, different correlation lengths are considered for random field discretisation. The spatial expectations and covariances for displacement response at any time instant are obtained as the output. The effect of open-storey is modelled by suitably considering the stiffness of infilled walls in the upper storey using cross bracing. In order to account for changes in soil conditions during strong motion earthquakes, both fixed and hinged supports are considered. The results of the stochastic finite element based seismic analysis of reinforced concrete framed structures reported in this paper demonstrate the importance of considering the effect of open-storey with appropriate support conditions to estimate the realistic response of buildings subjected to earthquakes.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.4
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• pp.222-231
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• 2016

Multi-member lattice-type structures including jackets and tripods are being considered as good alternatives to monopile foundations for relatively deep water of 25-50 m of water depth owing to their technical and economic feasibility. In this study, the reliability analysis of bottom-fixed offshore wind turbines with monopile and/or multi-member lattice-type foundations is carried out and the sensitivities of random variables such as material properties, external wind loadings and scouring depth are compared with respect to different types of foundations. Numerical analysis of the NREL 5 MW wind turbine supported by monopile, tripod and jacket substructures shows that the uncertainties of soil properties affect the reliability index more significantly for the monopile-supported OWTs while the reliability index is not so sensitive to the material properties in the cases of tripod- and jacket-supported OWTs. In conclusion, the reliability analysis can be preliminarily carried out without considering soil-pile-interaction in the cases of tripod- and jacket-supported OWTs while it is very important to use the well-measured soil properties for reliable design of monopile-supported OWTs.

• Analytical Science and Technology
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• v.35 no.6
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• pp.249-255
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• 2022

In this study, a new method for the analysis of high-purity nitrogen was developed. A gas chromatography-flame ionization detector (GC-FID) was used for purity analysis. Certified reference materials (CRMs) at a level of 3 µmol/mol of carbon monoxide (CO), carbon dioxide (CO₂), and methane (CH₄), which may exist in high-purity nitrogen, were prepared using the gravimetric method, and these CRMs were used for purity analysis. In this new method, ultra-high-purity and high-purity nitrogen were used as carrier gases. The impurities in high-purity nitrogen were quantitatively analyzed by comparing the differences in the area values of the GC chromatograms of the prepared CRMs. We purchased liquid nitrogen and three bottles of nitrogen gas, which were produced by three different manufacturers, using high-purity nitrogen. Furthermore, to validate the developed purity analysis method, the fraction of impurities in high-purity nitrogen was compared with the results of the typical purity analysis method. The comparison results were consistent within the expanded uncertainties (k = 2).

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.899-907
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• 2022

Purpose: Although many studies on seismic fragility analysis of general bridges have been conducted using machine learning methods, studies on curved bridge structures are insignificant. Therefore, the purpose of this study is to analyze the seismic fragility of bridges with I-shaped curved girders based on the machine learning method considering the material property and geometric uncertainties. Method: Material properties and pier height were considered as uncertainty parameters. Parameters were sampled using the Latin hypercube technique and time history analysis was performed considering the seismic uncertainty. Machine learning data was created by applying artificial neural network and response surface analysis method to the original data. Finally, earthquake fragility analysis was performed using original data and learning data. Result: Parameters were sampled using the Latin hypercube technique, and a total of 160 time history analyzes were performed considering the uncertainty of the earthquake. The analysis result and the predicted value obtained through machine learning were compared, and the coefficient of determination was compared to compare the similarity between the two values. The coefficient of determination of the response surface method was 0.737, which was relatively similar to the observed value. The seismic fragility curve also showed that the predicted value through the response surface method was similar to the observed value. Conclusion: In this study, when the observed value through the finite element analysis and the predicted value through the machine learning method were compared, it was found that the response surface method predicted a result similar to the observed value. However, both machine learning methods were found to underestimate the observed values.

• Korean Journal of Food Science and Technology
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• v.40 no.2
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• pp.152-159
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• 2008

The purpose of this study was to determine the accurate quantification of vitamin A in infant formula by comparing two different standard stock solutions as well as various sample weights using high performance liquid chromatography. The sources of uncertainty in measurement, such as sample weight, final smaple vloume, and the instrumental results, were identified and used as parameters to determine the combined standard uncertainty based on GUM(guide to the expression of uncertainty in measurement) and the Draft EURACHEM/CITAC Guide. The uncertainty components in measuring were identified as standard weight, purity, molecular weight, dilution of the standard solution, calibration curve, recovery, reproducibility, sample weight, and final sample volume. Each uncertainty component was evaluated for type A and type B and included to calculate the combined uncertainty. The analytical results and combined standard uncertainties of vitamin A according to the two different methods of stock solution preparation were 627 ${\pm}$ 33 ${\mu}$g R.E./100 g for 1,000 mg/L of stock solution, and 627 ${\pm}$ 49 ${\mu}$g R.E./100 g for 100 mg/L of stock solution. The analytical results and combined standard uncertainties of vitamin A according to the various sample weighs were 622 ${\pm}$ 48 ${\mu}$g R.E./100 g, 627 ${\pm}$ 33 ${\mu}$g R.E./100 g, and 491 ${\pm}$ 23 ${\mu}$g R.E./100 g for 1 g, 2 g, and 5 g of sampling, respectively. These data indicate that the preparation method of standard stock solution and the smaple amount were main sources of uncertainty in the analysis results for vitamin A. Preparing 1,000 mg/L of stock solution for standard material sampling rather than 100 mg, and sampling not more than 2 g of infant formula, would be effective for reducing differences in the results as well as uncertainty.

• Korean Journal of Food Science and Technology
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• v.36 no.5
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• pp.701-710
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• 2004

Uncertainty was quantified to evaluate calcium determination result in infant formula with AAS (Atomic Absorption Spectrometry) and ICP-AES (Inductively Coupled Plasma-Atomic Emission Spectrometry). Uncertainty sources in measurand, such as sample weight, final volume of sample, sample dilution and the instrumental result were identified and used as parameters for combined standard uncertainty based on the GUM (Guide to the expression of uncertainty in measurement) and Draft EURACHEM/CITAC Guide. Uncertainty components of each sources in measurand were identified as resolution, reproducibility and stability of chemical balance, standard material purity, standard material molecular weight, standard solution concentration, standard solution dilution factor, sample dilution factor, calibration curve, recovery, instrumental precision, reproducibility, and stability, Each uncertainty components were evaluated by uncertainty types and included to calculate combined uncertainty. The kinds of uncertainty sources and components in the analytical method by AAS and ICP-AES were same except sample dilution factor for AAS. The analytical results and combined standard uncertainties of calcium content were estimated within the certification range $(367{\pm}20\;mg/100g)$ of CRM (Certified Reference Material) and were not significantly different between method by AAS followed by ashing and method by ICP-AES followed by acid digestion as $359.52{\pm}23.61\;mg/100g\;and\;354.75{\pm}16.16\;mg/100g$, respectively. Identifying uncertainty sources related with precision, repeatability, stability, and maintaining proper instrumental conditions as well as personal proficiency was needed to reduce analytical error.