• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2600-2609
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• 2021

Low cycle fatigue behaviors of SA508-3 low-alloy steel were investigated in room-temperature air, high-temperature air and in light water reactor (LWR) water environments. The fatigue mean curve and design curve for the low-alloy steel are developed based on the fatigue data in room-temperature and high-temperature air. The environmental fatigue model for low-alloy steel is developed by the environmental fatigue correction factor (F_en) methodology based on the fatigue data in LWR water environments with the consideration of effects of strain rate, temperature, and dissolved oxygen concentration on the fatigue life.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1077-1092
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• 2007

The structural analysis model for estimate of load carrying capacity of railway bridge on service line is important to determine safety of bridges in service, we need to take response of bridge exactly, applying analysis model similar to the real railway bridge most. Track structure which is to distribute loads and decrease vibrations occurred from running train is constructed on the railway bridges. And it is important factor which should be considered to understand exact dynamic and static responses of bridge. But track structure is currently classified as a none structural members in the structural analysis model for estimating load carrying capacity of railway bridge and not considered in analysis model. That's the reason it is difficult to understand exact behavior of bridges. Therefore, the major objective of this study is to develop an analytical track-bridge model which is similar to real railway bridges considering track structure for safety assessment of railway bridge on service line to be effectively done.

• Earthquakes and Structures
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• v.11 no.5
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• pp.905-924
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• 2016

Several two-dimensional analytical beam column joint models with varying complexities have been proposed in quantifying joint flexibility during seismic vulnerability assessment of non-ductile reinforced concrete (RC) frames. Notable models are the single component rotational spring element and the super element joint model that can effectively capture the governing inelastic mechanisms under severe ground motions. Even though both models have been extensively calibrated and verified using quasi-static test of joint sub-assemblages, a comparative study of the inelastic seismic responses under nonlinear time history analysis (NTHA) of RC frames has not been thoroughly evaluated. This study employs three hypothetical case study RC frames subjected to increasing ground motion intensities to study their inherent variations. Results indicate that the super element joint model overestimates the transient drift ratio at the first story and becomes highly un-conservative by under-predicting the drift ratios at the roof level when compared to the single-component model and the conventional rigid joint assumption. In addition, between these story levels, a decline in the drift ratios is observed as the story level increased. However, from this limited study, there is no consistent evidence to suggest that care should be taken in selecting either a single or multi component joint model for seismic risk assessment of buildings when a global demand measure such as maximum inter-storey drift is employed in the seismic assessment framework.

• Journal of the Korea Institute of Building Construction
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• v.13 no.4
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• pp.416-423
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• 2013

An adjusted assessment model based on benefit-cost analysis (BCA) is proposed for evaluating the economic efficiency of automated construction technologies. In contrast to conventional BCA, the model does not compare monetary values, but the differences in benefits and costs between traditional and automated construction methods. To verify the usefulness of the model, it was applied to a real-scale building construction project that used a fully automated building construction system, and the face validity of the model was confirmed. The results indicate that the model can support decision makers in identifying valuable benefit factors and in assessing the cost effectiveness of the system.

• Journal of Food Hygiene and Safety
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• v.19 no.1
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• pp.19-24
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• 2004

Dose-response models in microbial risk assessment can be divided into biologically plausible models and empirical models. Biologically plausible models are formed by the assumptions in dose distribution of microbes, host sensitivity to microbes, and minimal infectious dose of microbes : there are Exponential model and $\beta$-Poisson model, representatively. Empirical models are mainly used to express the toxicity of chemicals : there are Weibull-Gamma model etc. Deviance function (Y) is used to fit available data to dose-response models, and some dose-response models for food-borne pathogens are developed in humans and experimental animals.

• Smart Structures and Systems
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• v.7 no.4
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• pp.263-274
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• 2011

A reliability-based slope stability assessment method considering fluctuations in the monitored matric suction was proposed for real-time identification of slope risk. The assessment model was based on the limit equilibrium model for infinite slope failure. The first-order reliability method (FORM) was adopted to calculate the probability of slope failure, and results of the model were compared with Monte-Carlo Simulation (MCS) results to validate the accuracy and efficiency of the model. The analysis shows that a model based on Advanced First-Order Reliability Method (AFORM) generates results that are in relatively good agreement with those of the MCS, using a relatively small number of function calls. The contribution of random variables to the slope reliability index was also examined using sensitivity analysis. The results of sensitivity analysis indicate that the effective cohesion c' is a significant variable at low values of mean matric suction, whereas matric suction ($u_a-u_w$) is the most influential factor at high mean suction values. Finally, the reliability indices of an unsaturated model soil slope, which was monitored by a wireless matric suction measurement system, were illustrated as 2D images using the suggested probabilistic model.

• Journal of Ecology and Environment
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• v.39 no.1
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• pp.71-80
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• 2016

The main goal of this study is to develop a multi-metric fishway assessment model (M_m-FA) and evaluate the efficiency of fishway. The M_m-FA model has three major fishway components with nine metrics: structural characteristics, hydraulic/hydrologic features, and biological attributes. The model was developed for diagnosing and assessing fishway efficiency and tested to Juksan Weir at the Yeongsan River Watershed. Structural characteristics of fishway included slope of the fishway (M₁), ratios of fishway width to stream width (M₂), and the proportion of orifice clogging and orifice size (M₃). Hydraulic/hydrologic characteristics included depth of fishway entrance head (M₄), depth of exit tail (M₅), and current velocity of inner fishway (M₆). Biological characteristics included fish species ratio of inner fishway to upper-lower weir (M₇), fish length distribution (M₈), and the proportion of migratory fish species to the total number of species (M₉). Overall, the assessment of fishway efficiency showed the total score of the M_m-FA model was 25 in the Juksan Weir, indicating "good condition" by the criteria of the five-level classification system. The M_m-FA model may be used as a key tool for the assessment of fishway efficiency, especially on the 16 weirs constructed for the "Four Rivers Restoration Project" after a partial calibration of M_m-FA model.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.2
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• pp.95-104
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• 2019

This study is aimed to take a stock assessment of blackthroat seaperch Doederleinia seaperch regarding the fishing effort of large-powered Danish Seine Fishery and Southwest Sea Danish Seine Fishery. For the assessment, the state-space model was implemented and the standardized catch per unit effort (CPUE) of large powered Danish Seine Fishery and Southwest Sea Danish Seine Fishery which is necessary for the model was estimated with generalized linear model (GLM). The model was adequate for stock assessment because its r-square value was 0.99 and root mean square error (RMSE) value was 0.003. According to the model with 95% confidence interval, maximum sustainable yield (MSY) of Blackthroat seaperch is from 2,634 to 6,765 ton and carrying capacity (K) is between 33,180 and 62,820. Also, the catchability coefficient (q) is between 2.14E-06 and 3.95E-06 and intrinsic growth rate (r) is between 0.31 and 0.72.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.2
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• pp.183-188
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• 2022

It is a difficult task to estimate parameters in even a simple stock assessment model such as a surplus production model, using only data about temporal catch-per-unit-effort (CPUE) (or survey index) and fishery yields. Such difficulty is exacerbated when time-varying parameters are treated as random effects (aka state variables). To overcome the difficulty, previous studies incorporated somewhat subjective assumptions (e.g., B₁=K) or informative priors of parameters. A key is how to build an objective joint prior of parameters, reducing subjectivity. Given the limited data on temporal CPUEs and fishery yields from 1999-2020 for common squid Todarodes pacificus, we built a joint prior of only two parameters, intrinsic growth rate (r) and carrying capacity (K), based on the resilience level of the population (Froese et al., 2017), and used a Bayesian state-space production assessment model. We used template model builder (TMB), a R package for implementing the assessment model, and estimating all parameters in the model. The predicted annual biomass was in the range of 0.76×10⁶ to 4.06×10⁶ MT, the estimated MSY was 0.13×10⁶ MT, the estimated r was 0.24, and the estimated K was 2.10×10⁶ MT.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2017-2025
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• 2021

Objective: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods: A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results: The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33-0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion: The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.