• Steel and Composite Structures
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• v.18 no.5
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• pp.1291-1303
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• 2015

Damage caused by low velocity impact is so dangerous in composites because although in most cases it is not visible to the eye, it can greatly reduce the strength of the composite material. In this paper, damage development in U-section glass/polyester pultruded beams subjected to low velocity impact was considered. Different failure criteria such as Maximum stress, Maximum strain, Hou, Hashin and the combination of Maximum strain criteria for fiber failure and Hou criteria for matrix failure were programmed and implemented in ABAQUS software via a user subroutine VUMAT. A suitable degradation model was also considered for reducing material constants due to damage. Experimental tests, which performed to validate numerical results, showed that Hashin and Hou failure criteria have better accuracy in predicting force-time history than the other three criteria. However, maximum stress and Hashin failure criteria had the best prediction for damage area, in comparison with the other three criteria. Finally in order to compare numerical model with the experimental results in terms of extent of damage, bending test was performed after impact and the behavior of the beam was considered.

• Journal of Korean Society of Forest Science
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• v.103 no.2
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• pp.233-239
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• 2014

This study was carried out to propose the criteria for the prediction of landslide occurrence through analysis the influence of each factor by using the quantification theory. The results obtained from this study are summarized as follows. From a stepwise regression analysis between the landslide area($m^2$) and environmental factors, the factors strongly affecting the landslide sediment($m^2$) were the Parents rock (igneous), cross slope(complex), coniferous forests (forest type) and slope gradient ($21{\sim}30^{\circ}$). According to the range, it was shown in order of Cross slope (0.2922), Parents rock (0.2691), Forest type (0.2631) and Slope gradient (0.2312). The range of prediction score of landslide occurrence has been distributed between score 0 and score 1.0556, the median value was score 0.5278. The prediction for class I was over 0.7818, for class II was 0.5279 to 0.7917, for class III 0.2694 to 0.5278 and for class IV was below 0.2693. The prediction on landslide occurrence appeared relatively high accuracy rate as 72% for class I and II. Therefore, this score table for landslide will be very useful for judgement of dangerous slope.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.2
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• pp.1-9
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• 2005

An end beam is one of the most important structural members supporting uncovered freight under in-service loading. In general, it needs to endure over 25 years. However fatigue fracture has occurred at dynamic stress concentration location of the end beam because user's specifications demanded high speed and vehicle manufacturer made the uncovered freight car with comparatively low strength and stiffness. For durability analysis, finite element analysis is performed to evaluate the problem of uncovered freight structure and local strain. The uncovered freight car was operated on actual problematic railroad line to measure dynamic stress versus time history on the critical part from which a crack is initiated often. Rainflow cycle counting method was used to estimate fatigue damage at dangerous area under operating condition. Therefore, this study shows that analytical fatigue life at the end beam can be predicted on the basis of S-N curve and structure analysis and has a fairly good correlation with experimental fatigue life.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.927-940
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• 2017

Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia.

• Journal of Navigation and Port Research
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• v.33 no.8
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• pp.519-524
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• 2009

Although it is expected to provide fundamental data for advanced VTS system by analyzing traffic accidents in VTS area, there is no quantitative analysis to find it.. In this research, it is examined and analyzed marine casualties records(1999-2004), data of Port-MIS and data of each VTS center. The results of this research are as below. 1) It is necessary to reduce traffic accident and to improve VTS operating system. 2) It is discovered for statistical discrepancy between vessels controlled by VTS and vessels not controlled by VTS in accident cause, visibility, perception distance and cause of late perception in collision accidents 3) It is necessary for VTS assistance to be positive and to made in ample time consecutively. 4) As the result of traffic accident prediction model, it is necessary to develop a system improving VTS operators' ability to identify dangerous ships.

• Journal of Korean Society of Forest Science
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• v.83 no.2
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• pp.175-190
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• 1994

In order to get fundamental information for prediction of landslide hazard, both forest and site factors affecting slope stability were investigated in many areas of active landslides. Twelve descriptors were identified and quantified to develop the prediction model by multivariate statistical analysis. The main results obtained could be summarized as follows : The main factors influencing a large scale of landslide were shown in order of precipitation, age group of forest trees, altitude, soil texture, slope gradient, position of slope, vegetation, stream order, vertical slope, bed rock, soil depth and aspect. According to partial correlation coefficient, it was shown in order of age group of forest trees, precipitation, soil texture, bed rock, slope gradient, position of slope, altitude, vertical slope, stream order, vegetation, soil depth and aspect. The main factors influencing a landslide occurrence were shown in order of age group of forest trees, altitude, soil texture, slope gradient, precipitation, vertical slope, stream order, bed rock and soil depth. Two prediction models were developed by magnitude and frequency of landslide. Particularly, a prediction method by magnitude of landslide was changed the score for the convenience of use. If the total store of the various factors mark over 9.1636, it is evaluated as a very dangerous area. The mean score of landslide and non-landslide group was 0.1977 and -0.1977, and variance was 0.1100 and 0.1250, respectively. The boundary value between the two groups related to slope stability was -0.02, and its predicted rate of discrimination was 73%. In the score range of the degree of landslide hazard based on the boundary value of discrimination, class A was 0.3132 over, class B was 0.3132 to -0.1050, class C was -0.1050 to -0.4196, class D was -0.4195 below. The rank of landslide hazard could be divided into classes A, B, C and D by the boundary value. In the number of slope, class A was 68, class B was 115, class C was 65, and class D was 52. The rate of landslide occurrence in class A and class B was shown at the hige prediction of 83%. Therefore, dangerous areas selected by the prediction method of landslide could be mapped for land-use planning and criterion of disaster district. And also, it could be applied to an administration index for disaster prevention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.570-576
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• 2020

This study uses beacons and smartphone Global Positioning System (GPS) receivers to establish a location-based disaster/hazard prediction system. Beacons are usually installed indoors to locate users using triangulation in the room, but this study is differentiated from previous studies because the system is used outdoors to collect information on registration location and temperature and humidity in hazardous areas. In addition, since it is installed outdoors, waterproof, dehumidifying, and dustproof functions in the beacons themselves are required, and in case of heat and humidity, the sensor must be exposed to the outside, so the waterproof function is supplemented with a separate container. Based on these functions, information on declining and vulnerable areas is identified in real time, and temperature/humidity information is collected. We also propose a system that provides weather and fine-dust information for the area concerned. User location data are acquired through beacons and smartphone GPS receivers, and when users transmit from declining or vulnerable areas, they can establish the data to identify dangerous areas. In addition, temperature/humidity data in a microspace can be collected and utilized to build data to cope with climate change. Data can be used to identify specific areas of decline in a microspace, and various analyses can be made through the accumulated data.

• Journal of Ginseng Research
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• v.39 no.2
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• pp.141-147
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• 2015

Background: Adipocytes, which are the main cellular component of adipose tissue, are the building blocks of obesity. The nuclear hormone receptor $PPAR{\gamma}$ is a major regulator of adipocyte differentiation and development. Obesity, which is one of the most dangerous yet silent diseases of all time, is fast becoming a critical area of research focus. Methods: In this study, we initially aimed to investigate whether the ginsenoside Rf, a compound that is only present in Panax ginseng Meyer, interacts with $PPAR{\gamma}$ by molecular docking simulations. After we performed the docking simulation the result has been analyzed with several different software programs, including Discovery Studio, Pymol, Chimera, Ligplus, and Pose View. All of the programs identified the same mechanism of interaction between $PPAR{\gamma}$ and Rf, at the same active site. To determine the drug-like and biological activities of Rf, we calculate its absorption, distribution, metabolism, excretion, and toxic (ADMET) and prediction of activity spectra for substances (PASS) properties. Considering the results obtained from the computational investigations, the focus was on the in vitro experiments. Results: Because the docking simulations predicted the formation of structural bonds between Rf and $PPAR{\gamma}$, we also investigated whether any evidence for these bonds could be observed at the cellular level. These experiments revealed that Rf treatment of 3T3-L1 adipocytes downregulated the expression levels of $PPAR{\gamma}$ and perilipin, and also decreased the amount of lipid accumulated at different doses. Conclusion: The ginsenoside Rf appears to be promising compound that could prove useful in antiobesity treatments.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.9-18
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• 2020

The purpose of this study is to suggest a method of predicting seismic vulnerability and safety conditions of each building in a targeted area. The scope of this study includes 'developing a simulation model for precaution activities,' 'testing the validity of the developed model', From the facility point of view, target of this study is a local building system. According to the literature review, the number of earthquake prediction modeling and cases with GIS applied is extremely few and the results are not proficient. This study is conducted as a way to improve the previous researches. Statistic analyses are conducted using 348 domestic and international data. Finally, as a result of the series of statistical analyses, an adequate model is developed using optimization scale method. The ratio of correct expectation is estimated as 87%. In order to apply the developed model to predict the vulnerability of the several chosen local building systems, spatial analysis technique is applied. Gangnam-gu and Jongro-gu are selected as the target areas to represent the characteristics of the old and the new downtown in Seoul. As a result of the analysis, it is discovered that buildings in Gangnam-gu are relatively more dangerous comparing to those of Jongro-gu and Eunpyeong-gu.