• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.961-965
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• 2000

This study aims to predict the particle size of blasted rock. For this purpose, Predicted particle sizes were compared with the measured particle sizes at the rock blasting sites, where various blasting patterns which controls the bench height, depth of blasted hole, burden, spacing etc were tested. the difference of mean fragment size between measured and predicted values was 0.11m.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.11 s.242
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• pp.1199-1208
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• 2005

The flow fields including velocities, turbulence intensities, Reynolds shear stress and turbulent kinetic energy were investigated using particle image velocimetry(PIV) to study the flow characteristics around two square cylinders in a tandem arrangement. The experiments were carried out in the range of the spacing from 1.0 to 4.0 widths of cylinder, Reynolds number of 5.3$\times$10$^{3}$ and 1.6$\times$10$^{4}$ respectively. Discontinuous jumping at the drag coefficient variation was found for two cylinders simultaneously when the spacing between two cylinders is varied. This phenomenon is attributed to a sudden change of the flow pattern which depends on the reattachment of the shear layer separated from the upstream cylinder. Near such a critical spacing, the changes of the flow fields as well as the effect of Reynolds number were studied in detail.

• 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.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.651-669
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• 2018

This study suggests the prediction model to estimate the specific energy of a pick cutter using a gene expression programming (GEP) and particle swarm optimization (PSO). Estimating the performance of mechanical excavators is of crucial importance in early design stage of tunnelling projects, and the specific energy (SE) based approach serves as a standard performance prediction procedure that is applicable to all excavation machines. The purpose of this research, is to investigate the relationship between UCS and BTS, penetration depth, cut spacing, and SE. A total of 46 full-scale linear cutting test results using pick cutters and different values of depth of cut and cut spacing on various rock types was collected from the previous study for the analysis. The Mean Squared Error (MSE) associated with the conventional Multiple Linear Regression (MLR) method is more than two times larger than the MSE generated by GEP-PSO algorithm. The $R^2$ value associated with the GEP-PSO algorithm, is about 0.13 higher than the $R^2$ associated with MLR.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.2
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• pp.235-242
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• 2010

An experimental investigation has been conducted to clarify roughness effects of geothermal water scale deposited onto a heating surface upon its forced convection heat transfer characteristics. Examined was a circular cylinder, on which particles of silica scale having five different sizes are uniformly distributed. The Reynolds number was varied from 13000 through 50000. Local and mean heat transfer characteristics were measured as functions of particle size and Reynolds number. Subsequently the mean fouling resistance was estimated from those results, and its characteristics are clarified. It was found that the heat transfer of cylinders greatly varies with the fouling of geothermal water scale, especially its scale height. Further, the local and average Nusselt numbers strongly depend upon the cylinder spacing and the Reynolds number.

• Journal of the Korean Ceramic Society
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• v.38 no.9
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• pp.799-805
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• 2001

Two commercial alumina powders having different particle size of $0.5{\mu}m$ and 3${\mu}$m were presintered at 1120$^{\circ}$C for 2h and then lanthanum aluminosilicate glass was infiltrated at 1100$^{\circ}$C for up to 4h to obtain the densified glass-alumina composites. The effect of alumina particle size on packing factor, microstructure, wetting, porosity and pore size, and mechanical properties of the composite was investigated. The optimum mechanical properties and compaction behavior were observed for the 3${\mu}$m alumina particle dispersed composite. The 3${\mu}$m alumina particle size and distribution for he preform were within 0.1 to 48${\mu}$m and bimodal and random orientation. The strength and the fracture toughness of the composite having 3${\mu}$m alumina particles were 519MPa and $4.5MPa{\cdot}m^{1/2}$, respectively.

• Korean Journal of Soil Science and Fertilizer
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• v.17 no.3
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• pp.212-217
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• 1984

Spatial variabilities of particle size distribution of 96 samples from Hwadong SiCL and Jungdong Sl were studied by using geostatistical concepts. The measurement was made at the nodes of the regular grid consisting of 12 rows and 8 columns. Sample spacing within rows and columns was 3 and 2 meters, respectively. The results are summarized as follows. 1. Variograms of Hwadong SiCL were fitted for the linear model and those of Jungdong SL for the spherical model. 2. Variograms of properties for Hwadong and clay for Jungdong showed the pure nugget effect. Those of silt and clay for Jungdong, however, appeared the nugget effect. 3. The minimum number of samples necessary to reproduce results similar to the true mean of the 96 measured values was approximately estimated. The minimum sample sizes of silt, clay, and sand in Hwadong SiCL were 27, 13, and 6, respectively. And the minimum sample size of clay in Jungdong SL was 17. 4. The approximate number of samples required to detect the difference of 5% of the true mean with 0.95 confidence level was estimated. The resulting number of samples for silt and sand in Jungdong was 14, and 26, respectively.

• Publications of The Korean Astronomical Society
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• v.21 no.2
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• pp.87-94
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• 2006

The COREA (COsmic ray Research and Education Array in Korea) project aims to build a ground array of particle detectors distributed over Korean Peninsular, through collaborations of high school students, educators, and university researchers, in order to study the origin of ultra high energy cosmic rays. COREA array will consist of about 2000 detector stations covering several hundreds of $km^2$ area at its final configuration and detect electrons and muons in extensive air-showers triggered by high energy particles. During the intial phase COREA array will start with a small number of detector stations in Seoul area schools. In this paper, we have studied by Monte Carlo simulations how to select detector sites for optimal detection efficiency for proton triggered air-showers. We considered several model clusters with up to 30 detector stations and calculated the effective number of air-shower events that can be detected per year for each cluster. The greatest detection efficiency is achieved when the mean distance between detector stations of a cluster is comparable to the effective radius of the air-shower of a given proton energy. We find the detection efficiency of a cluster with randomly selected detector sites is comparable to that of clusters with uniform detector spacing. We also considered a hybrid cluster with 60 detector stations that combines a small cluster with ${\Delta}{\iota}{\approx}100m$ and a large cluster with ${Delta}{\iota}{\approx}1km$. We suggest that it can be an ideal configuration for the initial phase study of the COREA project, since it can measure the cosmic rays with a wide range energy, i.e., $10^{16}eV{\leq}E{\leq}10^{19}eV$, with a reasonable detection rate.