• Geomechanics and Engineering
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• v.17 no.1
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• pp.31-45
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• 2019

In this study the spatial variability of soils is substantiated physically and numerically by using random field theory. Heterogeneous samples are fabricated by combining nine homogeneous soil clusters that are assumed to be elements of an adopted random field. Homogeneous soils are prepared by mixing different percentages of kaolin and bentonite at water contents equivalent to their respective liquid limits. Comprehensive characteristic laboratory tests were carried out before embarking on direct shear experiments to deduce the basic correlations and properties of nine homogeneous soil clusters that serve to reconstitute the heterogeneous samples. The tests consist of Atterberg limits, and Oedometric and unconfined compression tests. The undrained shear strength of nine soil clusters were measured by the unconfined compression test data, and then correlations were made between the water content and the strength and stiffness of soil samples with different consistency limits. The direct shear strength of heterogeneous samples of different stochastic properties was then evaluated by physical and numerical modelling using FISH code programming in finite difference software of $FLAC^{3D}$. The results of the experimental and stochastic numerical analyses were then compared. The deviation of numerical simulations from direct shear load-displacement profiles taken from different sources were discussed, potential sources of error was introduced and elaborated. This study was primarily to explain the mathematical and physical procedures of sample preparation in stochastic soil mechanics. It can be extended to different problems and applications in geotechnical engineering discipline to take in to account the variability of strength and deformation parameters.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2367-2375
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• 2022

In this work, we propose a new mechanistic model for the treatment of helium behaviour at the grain boundaries in oxide nuclear fuel. The model provides a rate-theory description of helium inter-granular behaviour, considering diffusion towards grain edges, trapping in lenticular bubbles, and thermal resolution. It is paired with a rate-theory description of helium intra-granular behaviour that includes diffusion towards grain boundaries, trapping in spherical bubbles, and thermal re-solution. The proposed model has been implemented in the meso-scale software designed for coupling with fuel performance codes SCIANTIX. It is validated against thermal desorption experiments performed on doped UO₂ samples annealed at different temperatures. The overall agreement of the new model with the experimental data is improved, both in terms of integral helium release and of the helium release rate. By considering the contribution of helium at the grain boundaries in the new model, it is possible to represent the kinetics of helium release rate at high temperature. Given the uncertainties involved in the initial conditions for the inter-granular part of the model and the uncertainties associated to some model parameters for which limited lower-length scale information is available, such as the helium diffusivity at the grain boundaries, the results are complemented by a dedicated uncertainty analysis. This assessment demonstrates that the initial conditions, chosen in a reasonable range, have limited impact on the results, and confirms that it is possible to achieve satisfying results using sound values for the uncertain physical parameters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.311-316
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• 2006

Despite geospatial information systems are widely used in many different fields as a powerful tool for spatial analysis and decision-making, their capabilities to handle realistic 3-D urban environment are very limited. The objective of this work is to integrate the recent developments in 3-D modeling and visualization into GIS to enhance its 3-D capabilities. To achieve a photorealistic view, building models are collected from a pair of aerial stereo images. Roof and wall textures are respectively obtained from ortho-rectified aerial image and ground photography. This study is implemented by using ArcGIS as the work platform and ArcObjects and Visual Basic as development tools. Presented in this paper are 3-D geometric modeling and its data structure, texture creation and its association with the geometric model. As the results, photorealistic views of Purdue University campus are created and rendered with ArcScene.

• Journal of Distribution Science
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• v.22 no.4
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• pp.1-10
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• 2024

Purpose: This study is aimed to introduce the application of phantom approach with structural equation modelling method for online learning. By integrating these innovative methodologies, the research seeks to advance the understanding of how the phantom approach can effectively complement and augment structural equation modeling techniques. Research design, data and methodology: A theoretical framework of Technology Acceptance Model (TAM) was modified and updated. A questionnaire was developed and used to extract information from 189 instructors who used online learning as their primary medium. The Covariance Based Structural Equation Modelling (CBSEM) was applied to test the direct effects and the phantom approach is used to handle the 2 mediators in the model. Results:social influence, perceived usefulness, and perceived ease of use exerted discernible impacts on instructors' intentionsto engage in online learning. These findings illuminate the intricate dynamics influencing instructor behavior within the realm of online education, underscoring the significance of various factors in shaping their intentions. Conclusions: In additions, the perceived usefulness and perceived ease of use had mediated the effect of social influence and instructor intention using phantom approach. Therefore, one can have concluded that this modified model was also confirmed, thereby reinforcing distribution strategies to online learning and overall education presence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6602-6609
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• 2013

This study is expected to provide background data for establishing mitigation measures for malodor and for comparing complex odor criteria. The impact of malodor at the afflicted locations was analyzed using Industrial Source Complex Short Term 3 (ISCST3) model, which was recommended by the EPA. The Odor Emission Rates (ODR) for piglets and hogs were predicted based on the average, minimum, and maximum emission rates as classification. The forecasting result of the complex odor modelling of pigsty showed that tolerance limit was exceeded at an adjacent administration building, but tolerance limit was not surpassed at an afflicted location which was within 185m from the pigsty. The ISCST3 modelling of the satisfactory ODR for tolerance limit was accomplished at the administration building. From the prediction of this modelling, maximum emission rates based on 1hr at administration building were 10.59~52.93, 19.05~31.76, and 10.59 $OU/m^3/s/m^2$ at emission rates of 50%, 30%, and 10%. This emission rate was slightly higher than the tolerance limit of 10.00 $OU/m^3/s/m^2$. However, it was inferred that the tolerance limit could be satisfied if the emission rate of 10% was controlled.

• International Journal of CAD/CAM
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• v.6 no.1
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• pp.9-17
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• 2006

Performing inspections for Hull Condition Monitoring and Assessment as stipulated in IACS unified requirements and IMO's Condition Assessment Scheme (CAS) IMO Resolution MEPC.94(46), 2001, Condition Assessment Scheme, IMO Resolution MEPC.111(50), 2003, Amendments to regulation 13G, addition of new regulation 13H involves a huge amount of measurement data to be collected, processed, analysed and maintained. Information to be recorded consists of thickness measurements and visual assessment of coating and cracks. The amount of data and increasing requirements with respect to condition assessment demand efficient computer support. Currently, due to the lack of standardization for this kind of data, the thickness measurements are recorded manually on ship drawings or tables. In this form, handling of the measurements is tedious and error-prone and assessment is difficult. Data reporting and analysis takes a long time, leading to some repairs being performed only at the next docking of the ship or making an additional docking necessary. The recently started ED funded project CAS addresses this topic and develops-as a first step-a data model for Hull Condition Monitoring and Assessment (HCMA) based on XML-technology. The model includes simple geometry representation to facilitate a graphically supported data collection as well as an easy visualisation of the measurement results. In order to ensure compatibility with the current way of working, the content of the data model is strictly confined to the requirements of the measurement process. Appropriate data interfaces to classification software will enable rapid assessment by the classification societies, thus improving the process in terms of time and cost savings. In particular, decision-making can be done while the ship is still in the dock for maintenance.

• The Journal of Engineering Geology
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• v.3 no.2
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• pp.125-148
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• 1993

Groundwater flow in fractured rock masses is controlled by combined effects of fracture networks, state of geostafic stresses and crossflow between fractures and rock matrix. Furthermore the scaie dependent, anisotropic properties of hydraulic parameters results mainly from irregular paftems of fracture system, which can not be evaluated properly with the methods available at present. The basic assumpfion of discrete fracture network model is that groundwater flows only along discrete fractures and the flow paths in rock mass are determined by geometric paftems of interconnected fractures. The characteristics of fracture distribution in space and fracture hydraulic parameters are represented as the probability density functions by stochastic simulation. The discrete fracture network modelling was aftempted to characterize the groundwater flow in the vicinity of existing large cavems located in Wonjeong-ri, Poseung-myon, Pyeungtaek-kun. The fracture data of $1\textrm{km}^2$ area were analysed. The result indicates that the fracture sets evaluated from an equal area projection can be grouped into 6 sets and the fracture sizes are distributed in longnormal. The conductive fracture density of set 1 shows the highest density of 0.37. The groundwater inflow into a carvem was calculated as 29ton/day with the fracture transmissivity of $10^{-8}\textrm{m}^2/s$. When the fracture transmissivity increases in an order, the inflow amount estimated increases dramatically as much as fold, i.e 651 ton/day. One of the great advantages of this model is a forward modelling which can provide a thinking tool for site characterization and allow to handle the quantitative data as well as qualitative data.

• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.100-110
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• 2020

Recently, the frequency of typhoon and torrential rain due to climate change is increasing. In addition, the upsurge in the complexity of urban sewer network and impervious surfaces area aggravates the inland flooding damage. In response to these worsening situations, the central and local governments are conducting R&D tasks related to predict and mitigate the flood risk. Researches on the analysis of inundation in urban areas have been implemented through various ways, and the common features were to evaluate the accuracy and justification of the model by comparing the model results with the actual inundation data. However, the evaluation procesure using available urban flooding data are not consistent, and if there are no quantitative urban inundation data, verification has to be performed by using press releases, public complaints, or photos of inundation occurring through 'CCTV'. Because theses materials are not quantitative, there is a problem of low reliability. Therefore, this study intends to develop a comparative analysis procedure on the quantitative degree and applicability of the verifiable inundation data, and a systematic framework for the performance assessment of urban flood analysis model was proposed. This would contribute to the standardization of the evaluation and verification procedure for urban flooding modelling.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.