• 대한기계학회논문집
• /
• 제18권5호
• /
• pp.1192-1202
• /
• 1994

Fatigue behavior and life prediction were presented for thermal-mechanical and isothermal low cycle fatigue of 12Cr forged steel used for high temperature applications. In-phase and out-of-phase thermal-mechanical fatigue test at 350 to 600.deg. C and isothermal low cycle fatigue test at 600.deg. C were conducted using smooth cylindrical hollow specimen under strain-control with total strain ranges from 0.006 to 0.015. Cyclic softening behavior was observed regardless of thermal-mechanical and isothermal fatigue tests. The phase difference between temperature and strain in thermal-mechanical fatigue resulted in significantly shorter fatigue life for out-of-phase than for in-phase. The difference in fatigue lives was dependent upon the magnitudes of inelastic strain ranges and mean stresses. Increase in inelastic strain range showed a tendency of intergranular cracking and decrease in fatigue life, especially for out-of-phase thermal-mechanical fatigue. Thermal-mechanical fatigue life prediction was made by partitioning the strain ranges of the hysteresis loops and the results of isothermal low cycle fatigue tests which were performed under the combination of slow and fast strain rates. Predicted fatigue lives for out-of-phase using the strain range partitioning method showed an excellent agreement with the actual out-of-phase thermal-mechanical fatigue lives within a factor of 1.5. Conventional strain range partitioning method exhibited a poor accuracy in the prediction of in-phase thermal-mechanical fatigue lives, which was quite improved conservatively by a proposed strain range partitioning method.

• Structural Engineering and Mechanics
• /
• 제67권2호
• /
• pp.105-113
• /
• 2018

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

• 대한기계학회논문집B
• /
• 제30권10호
• /
• pp.950-956
• /
• 2006

A mathematical heat transfer model for the prediction of heat flux on the slab surface and temperature distribution in the slab has been developed by considering the thermal radiation in the furnace and transient conduction governing equations in the slab, respectively. The furnace is modeled as radiating medium with spatially varying temperature and constant absorption coefficient. The slab is moved with constant speed through non-firing, charging, preheating, heating, and soaking zones in the furnace. Radiative heat flux which is calculated from the radiative heat exchange within the furnace modeled using the FVM by considering the effect of furnace wall, slab, and combustion gases is applied as the boundary condition of the transient conduction equation of the slab. Heat transfer characteristics and temperature behavior of the slab is investigated by changing such parameters as absorption coefficient and emissivity of the slab. Comparison with the experimental work shows that the present heat transfer model works well for the prediction of thermal behavior of the slab in the reheating furnace.

• 한국암반공학회:학술대회논문집
• /
• 한국암반공학회 2008년도 국제학술회의
• /
• pp.43-51
• /
• 2008

Currently an increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). For rational management of tunnels from planning to construction and maintenance stages, prediction, control and monitoring of displacements of and around the tunnel have to be performed with high accuracy. Computational method tools, such as finite element method, have been and are indispensable tool for tunnel engineers for many years. It is, however, a commonly acknowledged fact that determination of input parameters, especially material properties exhibiting nonlinear stress-strain relationship, is not an easy task even for an experienced engineer. Use and application of the acquired tunnel information is important for prediction accuracy and improvement of tunnel behavior on construction. Artificial Neural Network (ANN) model is a form of artificial intelligence that attempts to mimic behavior of human brain and nervous system. The main objective of this paper is to perform the deformation analysis in NATM tunnel by means of numerical simulation and artificial neural network (ANN) with field database. Developed ANN model can achieve a high level of prediction accuracy.

• 대한기계학회논문집
• /
• 제12권5호
• /
• pp.1097-1103
• /
• 1988

본 연구에서는 이 좁은 벤트내에 자료가 모이는 피로균열의 특성을 이용하여 표면피로균열의 성장거동을 파괴역학적으로 해석, 연구하여 균열의 성장특성과 $S-N_{f}$ 곡선의 추정을 마이크로 컴퓨터로 계산하였다.

• Corrosion Science and Technology
• /
• 제18권3호
• /
• pp.86-91
• /
• 2019

Generally, atmospheric corrosion is the electrochemical degradation of metal that can be caused by various corrosion factors of atmospheric components and weather, as well as air pollutants. Specifically, moisture and particles of sea salt and sulfur dioxide are major factors in atmospheric corrosion. Using galvanized steel is one of the most efficient ways to protect iron from corrosion by zinc plating on the surface of the iron. Galvanized steel is widely used in automobiles, building structures, roofing, and other industrial structures due to their high corrosion resistance relative to iron. The atmospheric corrosion of galvanized steel shows complex corrosion behavior, depending on the plating, coating thickness, atmospheric environment, and air pollutants. In addition, corrosion products are produced in different types of environments. The lifespans of galvanized steels may vary depending on the use environment. Therefore, this study investigated the corrosion behavior of galvannealed steel under atmospheric corrosion in two locations in Korea, and the lifespan prediction of galvannealed steel in rural and coastal environments was conducted by means of the potentiostatic dissolution test and the chemical cyclic corrosion test.

• Advances in concrete construction
• /
• 제11권2호
• /
• pp.111-126
• /
• 2021

Concrete cracking due to brittle tension strength significantly prevents fully utilization of the materials for "flexural-shear failure" type shear walls. Theoretical and experimental studies applying fiber reinforced concrete (FRC) have achieved fruitful results in improving the seismic performance of "flexural-shear failure" reinforced concrete shear walls. To come to an understanding of an optimal design strategy and find common performance prediction method for design methodology in terms to FRC shear walls, seismic performance on shear walls with PVA and steel FRC at edge columns and plastic region are compared in this study. The seismic behavior including damage mode, lateral bearing capacity, deformation capacity, and energy dissipation capacity are analyzed on different fiber reinforcing strategies. The experimental comparison realized that the lateral strength and deformation capacity are significantly improved for the shear walls with PVA and steel FRC in the plastic region and PVA FRC in the edge columns; PVA FRC improves both in tensile crack prevention and shear tolerance while steel FRC shows enhancement mainly in shear resistance. Moreover, the tensile strength of the FRC are suggested to be considered, and the steel bars in the tension edge reaches the ultimate strength for the confinement of the FRC in the yield and maximum lateral bearing capacity prediction comparing with the model specified in provisions.

• Geomechanics and Engineering
• /
• 제38권4호
• /
• pp.367-380
• /
• 2024

In this study, a prediction model for the cutting force evolution in brittle rocks was developed. This model is based on indentation tests using a conical pick at a cutting depth of 9 mm. The behavior of the indentation mechanism was analyzed in three phases by using Evans' cutting mode. The peak values in the force history identified these phases. The variation in the local strength of the rock caused a large offset in the model prediction of chipping. Regression analyses showed that there is a strong power relationship between the upper bound of the cutting force along with chipping and depth of cut. The slope of the three crushing phases has been found to increase sequentially (α₁<α₂<α₃). In addition, a positive correlation existed between the Schmidt hardness and brittleness index that affects the lower and upper bounds of chipping. Consequently, the results clearly demonstrate that the new model can reasonably predict the evolution of the cutting force based on experimental data. These results would be beneficial for engineers to design and select the optimum excavation machine to reduce mechanical vibration and enhance cutting efficiency.

• Geomechanics and Engineering
• /
• 제13권5호
• /
• pp.743-756
• /
• 2017

Laboratory investigation reveals that rockfills exhibit significant stress-path-dependent behavior during shearing, therefore realistic prediction of deformation of rockfill structures requires suitable constitutive models to properly reproduce such behavior. This paper evaluates the capability of a strain hardening model proposed by the authors, by comparing simulation results with large-scale triaxial stress-path test results. Despite of its simplicity, the model can simulate essential aspects of the shear behavior of rockfills, including the non-linear stress-strain relationship, the stress-dependence of the stiffness, the non-linear strength behavior, and the shearing contraction and dilatancy. More importantly, the model is shown to predict the markedly different stress-strain and volumetric behavior along various loading paths with fair accuracy. All parameters required for the model can be derived entirely from the results of conventional large triaxial tests with constant confining pressures.

• 한국정보시스템학회지:정보시스템연구
• /
• 제26권3호
• /
• pp.171-185
• /
• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.