• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.4 no.3
• /
• pp.75-83
• /
• 1984

The probability of failure is used to analyze the reliability of three dimensional slope failure, instead of conventional factor of safety. The strength parameters are assumed to be normal variated and beta variated. These are interval estimated under the specified confidence level and maximum likelihood estimation. The pseudonormal and beta random variables are generated using the uniform probability transformation method according to central limit theorem and rejection method. By means of a Monte-Carlo Simulation, the probability of failure is defined as; $P_f=M/N$ N: Total number of trials M: Total number of failures Some of the conclusions derived. from the case study include; 1. Three dimensional factors of safety are generally much higher than 2-D factors of safety. However situations appear to exist where the 3-D factor of safety can be lower than the 2-D factor of safety. 2. The $F_3/F_2$ ratio appears to be quite sensitive to c and ${\phi}$ and to the shape of the 3-D shear surface and the slope but not to be to the unit weight of soil. 3. From the two models (normal, beta) considered for the distribution of the factor of safety, the beta distribution generally provides lager than normal distribution. 4. Results obtained using the beta and normal models are presented in a nomgraph relating slope height and slop angle to probability of failure.

• Journal of Korean Society of Steel Construction
• /
• v.20 no.6
• /
• pp.723-730
• /
• 2008

Due to the higher ratio of live load to total loads of railway bridges, the accumulated damage by cyclic fatigue is significant. Moreover, it is highly possible that the initiated crack grows faster than that of highway bridges. Therefore, it is strongly needed to assess the safety for the accumulated damage analytically. The initiation and growth of fatigue-crack are related with the stress range, number of cycles, and the stiffness of the structural system. The stiffness of the structural system includes uncertainties of the planning, design, construction and maintenance, which varies as time goes. In this study, the authors developed the design and risk assessment techniques based on the reliability theories considering the uncertainties in load and resistance. For the probabilistic risk assessment of crack growth and the remaining life of the structures by the cyclic load of railway and subway bridges, response surface method (RSM) combined with first order second moment method were used. For composing limit state function, the stress range, stress intensity factor and the remaining life were selected as input important random variables to the RSM program. The probabilities of failure and the reliability indices of fatigue life for the considered specimen under cyclic loads were evaluated and discussed.

• Korean Journal of Materials Research
• /
• v.9 no.11
• /
• pp.1083-1087
• /
• 1999

Microstructures and the gas-sensing characteristics of the $\textrm{SnO}_2$ thin films were studied, which were deposited at various conditions (rf power, sample temperature, $\textrm{O}_2$/Ar ratio) by the rf magnetron sputtering. As a result, six typical microstructures were derived, such as amorphous(A), amorphous mixed with polycrystalline grains (A+P), polycrystalline with random crystalographic orientation (P), fine columnar (FC), coarse columnar (CC) and Zone T (T) with dense fiberous structure. Typically, A, A+ P, and P structures were formed when no $\textrm{O}_2$ was added to the sputter gas, whereas FC, CC, and T structures were obtained when $\textrm{O}_2$ was added. The A structure formed at low rf power and low temperature, the A+P at high rf power and low temperature, and the P at high rf power and high temperature. The FC structure was obtained at low rf power and low temperature. the CC at low rf power and high temperature, and the T at high rf power and low temperature. Results of the gas-sensing test of the sensor chips fabricated from the typical films indicated that the fine columnar microstructure shows the highest sensitivity both at $300^{\circ}C$ and $400^{\circ}C$. It was proposed that this is due to the high specific surface area of the micro-columns.

• Journal of Intelligence and Information Systems
• /
• v.26 no.3
• /
• pp.149-169
• /
• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."