• Journal of Korean Society of Forest Science
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• v.104 no.2
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• pp.198-205
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• 2015

The aim of this study was to provide the basic design parameters for developing logging residue compression machines by investigating compressive deformation characteristics of different types of logging residues. To achieve these objectives, Pinus rigida, Pinus koraensis and Quercus mongolica were selected as specimens, and compression-deformation tests by UTM(universial testing machine) were conducted. The experimental dataset were used to set up the model based on the compression-deformation ratio in the form of exponential function. The results showed that stress coefficient in terms of mechanical properties of logging residues was decreased, whereas strain coefficient tended to be increased as the number of compression increased at target density of $350kg/m^3$ and $400kg/m^3$. The model presented that the required stress was decreased as the number of compression increased, and the stress growth rate was swelled compared to the change of the deformation rate. Therefore, it showed that proper initial compression force was a significant variable in order to achieve the target density of logging residue.

• Journal of Forest and Environmental Science
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• v.17 no.1
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• pp.62-73
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• 2001

This study was conducted to predict the tractive resistance for tree length logs being skidded by ground based logging machine. The mathematical models for predicting the tractive resistance of tree length log have been developed. The tractive resistance is expressed as a function of log weight, skidding coefficient, and ground gradient. The skidding coefficients for four species of Korean pine, Japanese larch, mongolian oak, and cork oak were determined under laboratory condition using universal testing machine and small soil bin, Three different tractive resistance models were applied to four species and compared with each other. The ratios (T/Wt) of skidding-line tensions to the skidding log weight increased linearly with increment in ground gradient. Semi-ground skidding generally required smaller tensions than ground skidding under given condition. Results of this study can be utilized as basic information for logging machine selection and power requirement of skidding winch.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.43-49
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• 2023

Knowledge of minimum horizontal stress (S_hmin) is a significant step in determining full stress tensor. It provides crucial information for the production of sand, hydraulic fracturing, determination of safe mud weight window, reservoir production behavior, and wellbore stability. Calculating the S_hmin using indirect methods has been proved to be awkward because a lot of data are required in all of these models. Also, direct techniques such as hydraulic fracturing are costly and time-consuming. To figure these problems out, this work aims to apply the long-short-term memory (LSTM) algorithm to S_hmin time-series prediction. 13956 datasets obtained from an oil well logging operation were applied in the models. 80% of the data were used for training, and 20% of the data were used for testing. In order to achieve the maximum accuracy of the LSTM model, its hyper-parameters were optimized significantly. Through different statistical indices, the LSTM model's performance was compared with with other machine learning methods. Finally, the optimized LSTM model was recommended for S_hmin prediction in the well logging operation.

• Journal of Korean Society of Forest Science
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• v.86 no.4
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• pp.450-458
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• 1997

It is inadequate to compare operating costs between some logging machines with a low rate of utilization of them except the effect of operating time. The operating volume to maximize the utilization of the logging machines is different from each other, thus it is important to choose the proper logging machines and operating system according to the size of logging management. This study presents one of the methods to choose the economical optimum logging machines according to the operating volume to promote the economical efficiency of the machines. In order to go ahead with the Korean forestry mechanization plan, it should be considered which machine or operating system has the economical priority and if it can be suitable to the effect of wage increment.

• Economic and Environmental Geology
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• v.31 no.6
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• pp.527-534
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• 1998

Excavation of underground openings changes stress distribution around the opening. The survey of this disturbed zone in excavation is very important to design and construct underground facilities, such as tunnel, gas and oil storage, power plant and disposal site of high- and low-level radioactive wastes. This paper presents a zoning of rock masses with tunnel excavation using PS logging. Compressional and shear wave velocities are measured in boreholes drilled in the tunnel wall, which was constructed with blasting and/or machine excavation. The disturbed zone in excavation can be estimated by comparing PS logging data with a tomographic image of compressional wave velocity and compressional and shear wave velocities of core samples. In the side wall of tunnel, the disturbed zone reaches 1.5 m and 1.0 m in thickness for blocks of blasting and machine excavations, respectively. In the roof of tunnel, however, the disturbed zone is 1.0 m and 0.75 m thick for the two blocks. These results show that the width of the disturbed zone is larger in the side wall of tunnel than in the roof, and 1.3 to 1.5 times larger for the blasting excavation than for the machine excavation.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.85-97
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• 2003

This study was aimed at suggest a proper logging method of mountain forest using geographic information system(GIS) based on digital terrain model(DTM) in the National Forest at Mt. Kumsan in Namhae-gun, Gyungsangnam-do, which has about 2,948 ha in area. The areal percentage of 201 to 250m in the elevation was about 15.5%, elevation of 251 to 300m was 14.5%, and 78.75% for higher than 400m. The accumulated areal percentage of below 30% in the gradient was 17.2%, and 81.0% for steeper than 60%. The area for tractor skidding was 17.2%(511.7ha), the area for tractor attached winch skidding was 63.8%(1,896.3ha) and 18.4%(545.5ha) for cable yarding. It is important to choose the proper logging machines for timber harvesting. In general, the selection of logging operation system was affected several major environmental factors like as terrain conditions(slope gradient, slope length) and stand factors. The rate of middle slope gradients in terrain conditions showed higher than that of steep slope gradients in this area. Therefore, it considered that the logging operation system in this area could apply to tractor+winch operating machine according to terrain conditions.

• Journal of Soil and Groundwater Environment
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• v.21 no.3
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• pp.21-34
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• 2016

In the present study, we applied various machine learning techniques comparatively for prediction of subsurface structures based on multiple secondary information (i.e., well-logging data). The machine learning techniques employed in this study are Naive Bayes classification (NB), artificial neural network (ANN), support vector machine (SVM) and logistic regression classification (LR). As an alternative model, conventional hidden Markov model (HMM) and modified hidden Markov model (mHMM) are used where additional information of transition probability between primary properties is incorporated in the predictions. In the comparisons, 16 boreholes consisted with four different materials are synthesized, which show directional non-stationarity in upward and downward directions. Futhermore, two types of the secondary information that is statistically related to each material are generated. From the comparative analysis with various case studies, the accuracies of the techniques become degenerated with inclusion of additive errors and small amount of the training data. For HMM predictions, the conventional HMM shows the similar accuracies with the models that does not relies on transition probability. However, the mHMM consistently shows the highest prediction accuracy among the test cases, which can be attributed to the consideration of geological nature in the training of the model.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1708-1717
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• 2023

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F₁ score for both types of ores is greater than 0.9.

• Journal of Korean Society of Forest Science
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• v.105 no.2
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• pp.231-237
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• 2016

This research was conducted in order to examine whether the Woodgrab short-wood logging method, most widely used logging method in Korea, is more favorable than other logging methods in terms of productivity and profitability. For the comparative purposes, whole-tree logging methods with cable yarding system using a swing yarder and a tower yarder were evaluated. The productivity and the profitability of the logging operations by the machine types on a L. kaempferi stand were estimated by simulating logging processes based on bucking patterns and the results were compared. As a result, the Woodgrab short-wood logging system showed the most favorable results in terms of skidding productivity and operating cost. On the contrary, the system was the least profitable among the three logging methods. Main reason is that while the system may be beneficial in terms of operation productivity, it is restricted to produce only short logs mainly for low quality raw materials such as pulp, bolts, etc. which are sold at cheap prices.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.561-573
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• 2023

Korea Atomic Energy Research Institute (KAERI) constructed the KURT (KAERI Underground Research Tunnel) to analyze the hydrogeological/geochemical characteristics of deep rock mass. Numerous boreholes have been drilled to conduct various field tests. The selection of suitable investigation intervals within a borehole is of great importance. When objectives are centered around hydraulic flow and groundwater sampling, intervals with sufficient groundwater flow are the most suitable. This study defines such points as hydraulic outliers and aimed to detect them using borehole geophysical logging data (temperature and EC) from a 1 km depth borehole. For systematic and efficient outlier detection, machine learning algorithms, such as DBSCAN, OCSVM, kNN, and isolation forest, were applied and their applicability was assessed. Following data preprocessing and algorithm optimization, the four algorithms detected 55, 12, 52, and 68 outliers, respectively. Though this study confirms applicability of the machine learning algorithms, it is suggested that further verification and supplements are desirable since the input data were relatively limited.