• International Journal of Highway Engineering
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• v.13 no.4
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• pp.221-229
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• 2011

Road design needs to ensure the economic justification and the preservation of nature by adapting road alignment to the natural terrain. Though current road design guideline only defines a flat and a mountainous terrain, classification including rolling terrain should be needed while considering the fact that about 25.8% of our land can be classified as rolling and the road design guideline of developed countries such as United States and Australia has a terrain classification including rolling in order to take a deep consideration on the natural environment. The study attempts to draw a criterion to classify the assumed three individual terrains in a quantitative way by using a index like the undulation of the original ground profile. The study carried out a case study based on a conceptual frame developed in the study as an approach to differentiate each terrain. As a result, the study suggests a criterion in that a flat terrain has less than 40 meters in the difference between the highest and the lowest point of original ground from 40 to 60 meters for rolling terrain, and greater than 60 meters for mountainous respectively.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• Tunnel and Underground Space
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• v.28 no.1
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• pp.1-24
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• 2018

The use of TBM (tunnel boring machine) gradually increases in worldwide tunneling projects. TBM machine are often applied to more difficult and complex geological conditions in urban area, and many problems and difficulties have been reported due to these geological conditions. However, in Korea, there is a lack of research on difficult grounds so far. This paper discussed general aspects of investigation method, and problems of TBM tunneling in difficult grounds. Construction cases that passed through the difficult grounds in worldwide were analyzed and the typical difficult grounds were classified into 11 cases. For each case, the definition and general problems were summarized. Particularly, for mixed ground and boulder ground, and fault zone, which are frequent geological conditions in urban area with shallow depth, classification system, investigation methods and major considerations were discussed, and proposed the direction of future research. This paper is a basic study for the development of TBM construction technology in difficult ground, and it is expected that it will be useful for related research and construction of TBM in difficult ground in the future.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• Journal of the Korean Geotechnical Society
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• v.36 no.11
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• pp.21-33
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• 2020

Ground response analysis has been conducted for each borehole data in Pohang area, using 1D equivalent linear method program, to investigate the applicability of amplification factor to estimate peak ground acceleration. Earthquake motions for ground response analysis were prepared by matching response spectrums for return period of 500, 1000, and 2400 years suggested by seismic design code (MOIS, 2017). Ground survey data were acquired from Geotechnical Information DB System. It has been confirmed that response spectrum obtained from ground response analysis showed good agreement with those from seismic design code irrespective of ground classification. However, PGA (Peak Ground Accelerations) of ground response analysis did not coincide with PGA calculated using amplification factor suggested by seismic design code.

• Journal of the Korean Geotechnical Society
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• v.36 no.2
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• pp.5-15
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• 2020

The liquefaction phenomenon that occurred during the Pohang earthquake (M_L=5.4) brought new awareness to the people about the risk of liquefaction caused by the earthquake. Liquefaction hazard maps with 2 km grid made in 2014 used more than 100,000 borehole data for the whole country, and regions without soil investigation data were produced using interpolation. In the mapping of macro liquefaction hazard for the whole country, the site amplification effect and the ground water level 0 m were considered. Recently, the Ministry of Public Administration and Security (2018) published a new site classification method and amplification coefficient of the common standard for seismic design. Therefore, it is necessary to rewrite the liquefaction hazard map reflecting the revised amplification coefficient. In this study, the results of site classification according to the average shear wave velocity in soils before and after revision were compared in the whole country. Also, liquefaction assessment results were compared in Gangseo-gu, Busan. At this time, two ground accelerations corresponding to the 500 and 1,000 years of return period and two ground water table, 5 m for the average condition and 0 m the extreme condition were applied. In the drawing of liquefaction hazard map, a 500 m grid was applied to secure a resolution higher than the previous 2 km grid. As a result, the ground conditions that were classified as S_C and S_D grounds based on the existing site classification standard were reclassified as S₂, S₃, and S₄ through the revised site classification standard. Also, the result of the Liquefaction assessments with a return period of 500 years and 1,000 years resulted in a relatively overestimation of the LPI applied with the ground amplification factor before revision. And the results of this study have a great influence on the liquefaction assessment, which is the basis of the creation of the regional liquefaction hazard map using the amplification factor.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.92-100
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• 2012

A map of complex environment can be generated using a robot carrying sensors. However, representation of environments directly using the integration of sensor data tells only spatial existence. In order to execute high-level applications, robots need semantic knowledge of the environments. This research investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The proposed system is decomposed into five steps: sequential LIDAR scan, point classification, ground detection and elimination, segmentation, and object classification. This method could classify the various objects in urban environment, such as cars, trees, buildings, posts, etc. The simple methods minimizing time-consuming process are developed to guarantee real-time performance and to perform data classification on-the-fly as data is being acquired. To evaluate performance of the proposed methods, computation time and recognition rate are analyzed. Experimental results demonstrate that the proposed algorithm has efficiency in fast understanding the semantic knowledge of a dynamic urban environment.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.04a
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• pp.44-48
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• 2000

Several parameters to represent the characteristics of the observed at the domestic networks from several earthquakes occurred in the Korean Peninsula. Parameters to fit most the multiple Fourier amplitude spectra of the observed accelerations are estimated. This study adopts the stochastic ground motion model referred to the BLWN mode in which the energy is distributed randomly over the duration of the source and which has proven to be very effective in modeling a wide range of ground motion observations. The stochastic ground motion model employed here uses an omega-squared ({{{{ omega ^2 }}) Brune source model with a single corner frequency and a constant stress drop,. The {{{{ omega ^2 }} source model has become a seismological standard because of its simplicity an ability to predict spectral amplitudes and shapes over an extremely broad ranges of magnitudes distances and from the inversion show very unstable based on the fact of high values of mean/median. These results may imply that more observed data and more precise site classification including accurate preparation analysis of data such as more accurate scaling from counts to kine are needed for more stable are effective inversion of Fourier amplitude spectrum of the observed ground motions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.39-48
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• 2012

Filtering process that separates ground and non-ground points from LIDAR data is important in order to create the digital elevation model (DEM) or extract objects on the ground. The purpose of this research is to select the most effective filtering algorithm through qualitative and quantitative analysis for the existing filtering method used to extract ground points from LIDAR data. For this, four filtering methods including Adaptive TIN(ATIN), Perspective Center-based filtering method(PC), Elevation Threshold with Expand Window(ETEW) and Progressive Morphology(PM) were applied to mountain area, urban area and the area where building and mountains exist together. Then the characteristics for each method were analyzed. For the qualitative comparison of four filtering methods used for the research, visual method was applied after creating shaded relief image. For the quantitative comparison, an absolute comparison was conducted by using control points observed by GPS and a relative comparison was conducted by the digital elevation model of the National Geographic Information Institute. Through the filtering experiment of the LIDAR data, the Adaptive TIN algorithm extracted the ground points in mountain area and urban area most effectively. In the area where buildings and mountains coexist, progressive morphology algorithm generated the best result. In addition, as a result of qualitative and quantitative comparisons, the applicable filtering algorithm regardless of topographic characteristics appeared to be ATIN algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.1
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• pp.1-8
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• 2012

Obstacle detection is much studied by using sensors such as laser, vision, radar and ultrasonic in path planning for UGV(Unmanned Ground Vehicle), but not much reported about its characterization. In this paper not only an obstacle classification method using 2-dimensional LMS(Laser Measurement System) but also a decision making method whether to avoid or traverse the obstacle is proposed. The basic idea of decision making is to classify the characteristics by 2D laser scanned data and intensity data. Roughness features are obtained by range data using a simple linear regression model. The standard deviations of roughness and intensity data are used as measures for decision making by comparing with those of reference data. The obstacle classification and decision making for the UGV can facilitate a short path to the target position and the survivability of the robot.