• Tunnel and Underground Space
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• v.11 no.4
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• pp.344-351
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• 2001

In the tunnelling by blasting, the calculations of charge weight and the estimations of blasting effect have been simply carried out by empirical formulas. Also, it has been rare to consider the impact energy of blasting in numerical analyses. Thus in this study a numerical modeling technique of blasting load is developed and used with the 2 dimensional distinct element method(DEM) to consider the nonlinear behaviour of discontinuous underground structures. TD examine and verify its applicability of the numerical model to actual problems, a blasting of tunnel under an embankment is numerically analysed with DEM. It is examined that the behavior of circumference structures, the displacements of above- and under-ground structures, and the propagation of particle velocities can be known by this numerical analysis. As a result, the blasting load model, proposed by this study, can be applied to actual problems. This model applied with DEM can be used in the examination of structural stability.

• Tunnel and Underground Space
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• v.17 no.6
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• pp.438-447
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• 2007

This technical report is to introduce the research on SRM (Synthetic Rock Mass) which was presented in 2007 ISRM Congress at Lisbon by Prof, Fairhurst who speak with emphasis on its importance and potential in rock engineering. The Synthetic Rock Mass approach to jointed rock mass characterization (Pierce et al. 2007) is reviewed relative to existing empirical approaches and current understanding of jointed rock mass behaviour. The review illustrates how the key factors affecting the mechanical behaviour of jointed rock masses may be considered and demonstrates that the SRM approach constitutes a significant step forward in this field. This technique, based on two well-established methods, Bonded Particle Modelling in PFC-3D (Potyondy and Cundall, 2004) and Discrete Fracture Network simulation, employs a new sliding joint model that allows for large rock volumes containing thousands of pre-existing joints to be subjected to any non-trivial stress path. Output from SRM testing includes rock mass brittleness and strength, evolution of the full compliance matrix and primary fragmentation.

• Tunnel and Underground Space
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• v.19 no.2
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• pp.158-166
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• 2009

As a basic study for investigating the development of the stress-induced crack in Hoek-Brown rock, a homogenization technique of elastic cracks is proposed. The onset of crack is monitored by Hoek-Brown empirical criterion, while the orientation of the crack is determined by the critical plane approach. The concept of volume averaging in stress and strain component was invoked to homogenize the representative rock volume which consists of intact rock and cracks. The formulation results in the constitutive relations for the homogenized equivalent anisotropic material. The homogenization model was implemented in the standard FEM code COSMOSM. The numerical uniaxial tests were performed under plane strain condition to check the validity of the propose numerical model. The effect of friction between the loading plate and the rock sample on the mode of deformation and fracturing was examined by assuming two different contact conditions. The numerical simulation revealed that the homogenized model is able to capture the salient features of deformation and fracturing which are observed commonly in the uniaxial compression test.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Journal of Distribution Research
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• v.5 no.2
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• pp.49-68
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• 2001

In this chapter, we summarize the results on the optimal location selection and present limitation and direction of research. In order to reach the objective, this study selected and tested the interaction model which obtains the value of co-ordinates on location selection through the optimization technique. This study used the original variables in the model, but the results indicated that there is difference in reality. In order to overcome this difference, this study peformed market survey and found the new variables (first data such as price, quality and assortment of goods, and the second data such as aggregate area, and area of shop, and the number of cars in the parking lot). Then this study determined an optimal variable by empirical analysis which compares an actual value of market share in 1988 with the market share yielded in the model. However, this study found the market share in each variables does not reflect a reality due to an assumption of λ-value in the model. In order to improve this, this study performed a sensitivity analysis which adds the λ value from 1.0 to 2.9 marginally. The analyzed result indicated the highest significance with the market share ratio in 1998 at λ of 1.0. Applying the weighted value to a variable from each of the first data and second data yielded the results that more variables from the first data coincided with the realistic rank on sales. Although this study have some limits and improvements, if a marketer uses this extended model, more significant results will be produced.