• Geomechanics and Engineering
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• v.35 no.5
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• pp.539-554
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• 2023

This article is dedicated to the pursuit of establishing a robust empirical relationship that allows for the estimation of in-situ modulus of deformations (E_m and G_m) within sedimentary rock slope masses through the utilization of Q_slope values. To achieve this significant objective, an expansive and thorough methodology is employed, encompassing a comprehensive field survey, meticulous sample collection, and rigorous laboratory testing. The study sources a total of 26 specimens from five distinct locations within the South Pars (known as Assalouyeh) region, ensuring a representative dataset for robust correlations. The results of this extensive analysis reveal compelling empirical connections between E_m, geomechanical characteristics of the rock mass, and the calculated Q_slope values. Specifically, these relationships are expressed as follows: E_m = 2.859 Q_slope + 4.628 (R² = 0.554), and G_m = 1.856 Q_slope + 3.008 (R² = 0.524). Moreover, the study unravels intriguing insights into the interplay between in-situ deformation moduli and the widely utilized Rock Mass Rating (RMR) computations, leading to the formulation of equations that facilitate predictions: RMR = 18.12 E_m0.460 (R² = 0.798) and RMR = 22.09 G_m0.460 (R² = 0.766). Beyond these correlations, the study delves into the intricate relationship between RMR and Rock Quality Designation (RQD) with Qslope values. The findings elucidate the following relationships: RMR = 34.05e^0.33Qslope (R² = 0.712) and RQD = 31.42e^0.549Qslope (R² = 0.902). Furthermore, leveraging the insights garnered from this comprehensive analysis, the study offers an empirically derived support system tailored to the distinct characteristics of discontinuous rock slopes, grounded firmly within the framework of the Q_slope methodology. This holistic approach contributes significantly to advancing the understanding of sedimentary rock slope stability and provides valuable tools for informed engineering decisions.

• Tunnel and Underground Space
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• v.18 no.6
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• pp.427-435
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• 2008

In this paper, review was made for the excavation method and optimum bench length for unstable tunnel face in case of rock classification type V in order to make the best use of in-situ bearing capacity. 3D FEM analyses were performed to investigate the influences on the tunnel face and adjacent area with regard to the pattern and number of bolts when face bolts were used as a supplementary measure. As a result of this study, full section excavation method with sub-bench is effective in reducing the displacement greatly due to early section closure. Displacement-resistant effects in accordance with the bolting patterns are grid type, zig-zag type and then circular type in order of their effect. And horizontal extrusion displacement of tunnel face reduces as the number of bolts increase. A grid type face bolt covering $1.5m^2$ of tunnel face could secure the face stability in case of full section excavation method with sub-bench.

• Geomechanics and Engineering
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• v.11 no.4
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• pp.515-530
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• 2016

In this paper, five different quantitative parameters were proposed for the characterization of the primary roughness which is the component of surface morphology that prevails during large strike-slip faults of more than 50 m. These parameters are mostly the anisotropic properties of rock surface morphology at various scales: (i) coefficient ($k_a$) and degree (${\delta}_a$) of apparent structural anisotropy of surface; (ii) coefficient ($k_r$) and degree (${\delta}_r$) of real structural anisotropy of surface; (iii) surface anisotropy function P(${\varphi}$); and (iv) degree of surface waviness ($W_s$). The coefficient and degree of apparent structural anisotropy allow qualifying the anisotropy/isotropy of a discontinuity according to a classification into four classes: anisotropic, moderately anisotropic/isotropic and isotropic. The coefficient and degree of real structural anisotropy of surface captures directly the actual surface anisotropy using geostatistical method. The anisotropy function predicts directional geometric properties of a surface of discontinuity from measurements in two orthogonal directions. These predicted data may subsequently be used to highlight the anisotropy/isotropy of the surface (radar plot). The degree of surface waviness allows qualifying the undulation of anisotropic surfaces. The proposed quantitative parameters allows their application at both lab and field scales.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.277-289
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• 2019

This study was designed to analyze vegetation units using a phytosociological method and to identify the ecological characteristics of each vegetation unit, for forest vegetation from Misiryeong to Danmokryeong of Baekdudaegan, in which, in total, 150 plots were surveyed during May to October 2016. Using community classification according to phytosociology, the Quercus mongolica community group was classified at the top level of a vegetation hierarchy that was classified into an Abies koreana community and a Carpinus cordata community. The A. koreana community was divided into Thuja koraiensis and A. koreana typical groups. The T. koraiensis group was subdivided into Pinus pumila and Betula chinensis subgroups. The C. cordata community was divided into Sasa borealis and C. cordata typical groups. Thus, this forest vegetation comprised one community group, two communities, four groups, and two subgroups and indicated five vegetation units. After analyzing the correlations among the five vegetation units classified by this plant sociological method and the environmental factors like altitude, bare rock, number of present species, and coverage of tree layer with a coincidence method, the A. koreana community and C. cordata typical group were found to be distributed above 1,000 m in altitude, and the S. borealis group was distributed below 1,000 m in altitude. Except for vegetation unit 1, vegetation units tended to be mainly distributed where there was less than 20% bare rock. There was no typical tendency in the number of species present; vegetation unit 5 showed the most abundance among the vegetation units. Coverage by the tree layer mostly exceeded 60%, except for vegetation unit 1.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.6
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• pp.13-21
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• 2016

Feature selection is the one of important issue in the field of data mining and machine learning. It is the technique to find a subset of features which provides the best classification performance, from the source data. We propose a feature subset selection method using the neighborhood rough set model based on information granularity. To demonstrate the effectiveness of proposed method, it was applied to select the useful features associated with breast tumor diagnosis of 298 shape features extracted from 5,252 breast ultrasound images, which include 2,745 benign and 2,507 malignant cases. Experimental results showed that 19 diagnostic features were strong predictors of breast cancer diagnosis and then average classification accuracy was 97.6%.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.17-30
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• 2021

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.10a
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• pp.275-280
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• 2001

Although important developments have taken place since ESOPT 1974 both with respect to the test method as well as the interpretation of the results, many uncertainties still remain in the Standard Penetration Test(SPT). The main pitfall of SPT is that it has not been standardized differing from its terminology and further, the possibility of standardization is very low in practice. Therefore, lack of knowledge on the equipment and method of SPT tends to cause some errors in interpretation of the results. It Is especially important to understand this tendency in domestic design, because most foundations are designed based on SPT results only. Many researchers have made an effort to minimize the uncertainties of SPf in Korea, it is not cleary defined what the most effective method of execution and interpretation of SPT Some uncertainties which many geotechnical engineers encounter in practice are introduced to discuss about improvement of test procedure and interpretation.

• The Journal of Engineering Geology
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• v.17 no.1 s.50
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• pp.89-99
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• 2007

This paper intends to introduce more objective and qualitative rock mass classification method easily applicable to the excavation of gneissic masses showing corestone weathering profiles. It is proven that corestone weathering profile could be divided with reasonable accuracy into digging, ripping and blasting layers using visual and simple mechanical techniques such as Schmidt hammer rebound test on cut slopes, taking into consideration strength and spacial distribution of corestone, workability and work efficiency of excavation. Also, seismic refraction surveys were employed for shallow investigations (down to $20{\sim}30m$ depth) in corestone weathering profile and conducted across the top of vertical exposures where the underlying geology could be directly inspected. Some discrepancies ($3{\sim}4m$ in average and 6 m occasionally) between the actual and assumed materials with respect to seismic velocities were observed. Thus it can be concluded that field geotechnical mapping and field seismic test should be used together in order to get a relatively good accuracy in assessing likely excavation conditions of corestone weather-ing profiles.

• 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.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.