• Explosives and Blasting
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• v.35 no.4
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• pp.27-35
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• 2017

A total of 22 sites around openings of limestone mine are chosen to assess rock mass classification schemes such as RMR, Q-system, and GSI. RMR and Q are modified to estimate the relationship with GSI. Q' is the modified Q with SRF=1.0 and $J_w=1.0$. Rock mass is assumed to be completely dry and very favorable discontinuity orientations are assumed to estimate ${RMR_{89}}^{\prime}$. Relationships of Q-Basic RMR, Q-Total RMR, ${GSI-RMR_{89}}^{\prime}$, and GSI-Q' are analyzed, in which a correlation of ${GSI-RMR_{89}}^{\prime}$ is found to be the highest. Failure strains are calculated using the modulus ratios and most measuring sites appear to be stable with low failure strain class.

• Journal of the Korean Geotechnical Society
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• v.15 no.3
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• pp.177-197
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• 1999

The measurement of sonic velocities is commonly used as an index of engineering properties of rock, but it is not widely appreciated that this velocity can change markedly with the degree of saturation of the sample. This paper records the nature of this variation as seen in samples of Korean granite. The ISRM method of testing suggested for this index can also create difficulties, especially if vaseline is used as a coupling agent, and invades the samples, and if the sample volume changes with degree of saturation. Careful measurements of the natural variation in sonic velocity that occur in a sample whose saturation is gradually increased may be a means of assessing the relic stresses within it.

• 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.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.4
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• pp.245-256
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• 2016

In the companion paper (I - Database and Site Response Analyses), site-specific response analyses were performed at more than 300 domestic sites. In this study, a new site classification system and design response spectra are proposed using results of the site-specific response analyses. Depth to bedrock (H) and average shear wave velocity of soil above the bedrock ($V_{S,Soil}$) were adopted as parameters to classify the sites into sub-categories because these two factors mostly affect site amplification, especially for shallow bedrock region. The 20 m of depth to bedrock was selected as the initial parameter for site classification based on the trend of site coefficients obtained from the site-specific response analyses. The sites having less than 20 m of depth to bedrock (H1 sites) are sub-divided into two site classes using 260 m/s of $V_{S,Soil}$ while the sites having greater than 20 m of depth to bedrock (H2 sites) are sub-divided into two site classes at $V_{S,Soil}$ equal to 180 m/s. The integration interval of 0.4 ~ 1.5 sec period range was adopted to calculate the long-period site coefficients ($F_v$) for reflecting the amplification characteristics of Korean geological condition. In addition, the frequency distribution of depth to bedrock reported for Korean sites was also considered in calculating the site coefficients for H2 sites to incorporate sites having greater than 30 m of depth to bedrock. The relationships between the site coefficients and rock shaking intensity were proposed and then subsequently compared with the site coefficients of similar site classes suggested in other codes.

• 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.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.530-548
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• 2022

The site selection process for deep geological disposal of high-level radioactive waste will be conducted in stages, and 103 evaluation parameters related to site selection have been proposed. In the field of rock mechanics and rock engineering, there are 33 evaluation parameters for intact rock, joint and rock mass, and they are applied in the basic and detailed investigation stages. In this report, uniaxial compressive strength, in-situ stress, joint distribution, and rock mass classification were selected as the main evaluation parameters, and among them, uniaxial compressive strength and in situ stress were selected as key evaluation parameters. Statistical techniques or regression analysis were performed for granite in Wonju and Chuncheon to evaluate the distribution range for the selected key evaluation parameters. The average of the uniaxial compressive strength in the Wonju area estimated through the posterior distribution is about 171 MPa, and about 123 MPa in the Chuncheon area. The maximum in situ stress acting in the Wonju area was less than 30 MPa and less than 40 MPa in the Chuncheon area. The direction of the maximum horizontal stress calculated by regression analysis was 101° in Wonju, and in the case of Chuncheon, it was 95°, respectiviely.

• Tunnel and Underground Space
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• v.13 no.1
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• pp.6-19
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• 2003

This study has for its object to increase an objectivity of the observation result in the face mapping of tunnel and to suggest the reasonable support and reinforcement methods to be considered the rock properties. It was developed in this study to the tunnel stability evaluation system(Prototype NFEST) to be used fuzzy set theory and neuro-fuzzy techniques, and this system was verified according to the reliability evaluation between the 36 learning data and the inferred results. When it summarized the results; (1) 12 evaluation items and ranges were proposed to be modified basis on the RMR which are well known to the domestic workers. (2) It was shown that correlation coefficient(│R│) between $RMR_{inf}$ inferred by 12 items and $RMR_{org}$ due to arithmetic total, $RMR_{chk}$ due to subjective judgement of observer are relatively high relationship with each 0.83 and 0.79. (3) Inferred result of the total tunnel safety shows also a good relationship with $RMR_{inf}$ (│R│=0.7) and the rock weathering(│R│=0.84).

• Tunnel and Underground Space
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• v.22 no.5
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• pp.297-309
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• 2012

The stability assessment of abandoned gangway for the purpose of services was performed. Among the many factors that affect the stability of openings, the span of the opening in a given rock mass condition provides an important element of design. In this paper, the stability of gangway was assessed by the critical span curves proposed by Lang, the modified Mathews'stability graph method and using support measures of the Q system. In the evaluation of stability as a whole the gangway is considered as stable. But the rockfalls of wedge-shaped blocks were expected in the area in which the horizontal joints of low angle appear. The support measures such as local rock bolts are required to use for commercial purposes of the abandoned gangway. And entrance section may require the particular attention as unstable section. Since there are so many spalling due to bad blasting in the roof and sidewall of gangway, the scaling operations should be followed primarily.