• The Journal of Engineering Geology
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• v.30 no.1
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• pp.31-42
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• 2020

Electrical resistivity and downhole seismic surveys were conducted together with bore investigations and well-logging to examine subsurface structures in small-scale landslides at Sinjindo-ri, Geunheung-myeon, Taean-gun, Chungcheongnam-do, Republic of Korea in 2014. On the basis of the low N-values at depths of 5~7 m in borehole BH-2, downhole seismic and electrical dipole-dipole resistivity surveys were performed to delineate geological weak zones. The low-resistivity zones (<150 Ω·m) measure ~8 m in thickness and show a close depth correspondence to weathered soils consisting mainly of silty clays as identified from the bore investigations and well-logging data. Compared with weak zones in borehole BH-1, weak zones in BH-2 are characterized by lower densities (1.6~1.8 g/㎤) and resistivities (<150 Ω·m) and greater variation in Poisson's ratio. These observations can be explained by the presence of wet silty clays rich in weathered soil material that have resulted from heavy rainfall and rises in groundwater level. Downslope movements are probably caused by the sliding of wet clay that acts to reduce the strength of the weathered soil.

• Tunnel and Underground Space
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• v.16 no.2 s.61
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• pp.166-178
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• 2006

Recently, the frequency of occurring dynamic events such as earthquakes, explosives blasting and other types of vibration has been increasing. Besides, the chances of exposure for rock discontinuities to free faces get higher as the scale of rock mass structures become larger. For that reason, the frictional behavior of rock joints under dynamic conditions needs to be investigated. In this study, artificially fractured rock joint specimens were prepared in order to examine the dynamic frictional behavior of rough rock joint. Roughness of each specimen was characterized by measuring surface topography using a laser profilometer and a series of shaking table tests was carried out. For mated joints, the static friction angle back-calculated ken the yield acceleration was $2.7^{\circ}$ lower than the tilt angle on average. The averaged dynamic friction angle for unmated joints was $1.8^{\circ}$ lower than the tilt angle. Displacement patterns of sliding block were classified into 4 types and proved to be related to the first order asperity of rock joint. The tilt angle and the static friction angle for mated joints seem to be correlated to micro average inclination angle which represents the second order asperity. The tilt angle and the dynamic friction angle for unmated Joints, however, have no correlation with roughness parameters. Friction angles obtained by shaking table test were lower than those by direct shear test.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.