• Composites Research
• /
• v.17 no.5
• /
• pp.39-46
• /
• 2004

In this study, the strength of spatially reinforced composites (SRC) are predicted by using stiffness reduction for each structural element composed of a rod stiffness in each direction and a matrix stiffness proportional to its rod volume fraction. Maximum failure strain criteria is applied to rod failure, and modified Tsai-Wu failure criteria to matrix failure. The material properties composed of the tensile failure strain of a rod, the compressive failure strain of 3D SRC, the tensile and compressive strength of the 3D SRC in the $45^{\cir}$ rotated direction from a rod and the shear strength of the 3D SRC are measured to predict the SRC strength. The strength distributions of the 3D/4D SRC in rod and off-rod direction have the largest and the smallest values, respectively. A variable load step is selected to increase an efficiency of strength distribution calculation. Uniform load step is applied when a load history is needed. The results of compressive strength from analysis and experiment show the 18 % difference though the initial slop is coincident with each other.

• Journal of the Korean Wood Science and Technology
• /
• v.37 no.5
• /
• pp.465-473
• /
• 2009

Three failure theories were studied to evaluate the wood strength variation with grain slope. Maximum stress theory, Tsai-Hill theory and Hankinson formula were presented to hypothesize the failure of wood according to grain slope to loading direction. Red pine and Japanese larch were used as materials to simulate failure strength prediction with grain slope. Calculation of strength results was that the strength of wood drops rapidly between parallel to grain orientation (0 degree) and 15 degree grain orientation. The strength of wood with grain orientation were somewhat different at small grain angles among failure theories, and this tendency was due to tension and compression distinction, and shear accounting in each theories. For the above 45 degree grain orientation, the predicted failure strength of wood with grain variation were very close in each failure theories and were useful in assessing failure strength of wood. The applicable these theories should be considered that the wood has different behavior in tension and compression, and this lead to different strength at small grain angles in each theories. Furthermore, reconsideration is needed to assess the failure strength of wood at small grain angles in Hankinson formula and further studies are necessary to accounting for shear behavior at small grain angles.

• Journal of the Korean Society of Safety
• /
• v.27 no.4
• /
• pp.115-120
• /
• 2012

In order to establish preventive maintenance standards through analysis & reliability prediction of about 60,000pcs of 20kindsof relays and contractors used for Seoul subway trains, several life prediction methodologies were applied. Firstly, Occurrence, Severity, Detection were defined and predicted by applying operation characteristic of EMU to the number of actions of relays & contactors which the manufacturers generally offer as the life cycle data. Secondly, failure distribution and average life of parts were analyzed through interpretation of field data based on a lot of experience which had built up in the field for a long time. Finally, using the 217PLUS standard as a reliability prediction program, comparative analysis of use reliability and inherent reliability was done through reliability prediction at the part level and system level.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.33 no.2
• /
• pp.1-16
• /
• 2008

The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.

• Journal of the Korean Geosynthetics Society
• /
• v.16 no.3
• /
• pp.1-10
• /
• 2017

Recently, one of the natural disasters, landslide is causing huge damage to people and properties. In order to minimize the damage caused by continuous landslide, a scientific management system is needed for technologies related to measurement and monitoring system. This study aims to establish a management system for landslide damage by prediction of slope failure. Ground behavior was predicted by surface ground deformation in case of slope failure, and the change in ground displacement was observed as slope surface. As a result, during the slope failure, the ground deformation has the collapse section, the after collapse precursor section, the acceleration section and the burst acceleration section. In all cases, increase in displacement with time was observed as a slope failure, and it is very important event of measurement and maintenance of risky slope. In the future, it can be used as basic data of slope management standard through continuous research. And it can contribute to reduction of landslide damage and activation of measurement industry.

• The Journal of Engineering Geology
• /
• v.20 no.3
• /
• pp.243-255
• /
• 2010

The slope design of an open-pit mine must consider economical efficiency and stability. Thus, the overall slope angle is the principal factor because of limited support or reinforcement options available in such a setting. In this study, slope displacement, as monitored by a GPS system, was analyzed for a coal mine at Pasir, Indonesia. Predictions of failure time by inverse velocity analysis showed good agreement with field observations. Therefore, the failure time of an unstable slope can be roughly estimated prior to failure. A GIS model that combines fuzzy theory and the analytical hierarchy process (AHP) was developed to assess slope instability in open-pit coal mines. This model simultaneously considers seven factors that influence the instability of open-pit slopes (i.e., overall slope gradient, slope height, surface flows, excavation plan, tension cracks, faults, and water body). Application of the proposed method to an open-pit coal mine revealed an enhanced prediction accuracy of failure time and failure site compared with existing methods.

• Steel and Composite Structures
• /
• v.39 no.2
• /
• pp.201-213
• /
• 2021

Currently, there is a lack of research in the design approach to avoid column wall failure in the concrete filled double skin steel tubular (CFDST) column-beam connections. In this paper, a finite element model has been developed and verified by available experimental data to analyze the failure mechanism of CFDST column-beam connections. Various finite element models with different column hollow ratios (χ) were established. The simulation result revealed that with increasing χ the failure mode gradually changed from yielding of end plate, to local failure of the column wall. Detailed parametric analyses were performed to study the failure mechanism of column wall for the CFDST column-beam connection, in which the strength of sandwiched concrete and steel tube and thickness of steel tube were incorporated. An analytical model was proposed to predict the moment resistance of the assembled connection considering the failure of column wall. The simulation results indicate that the proposed analytical model can provided a conservative prediction of the moment resistance. Finally, an upper bound value of χ was recommend to avoid column wall failure for CFDST column-beam connections.

• Structural Monitoring and Maintenance
• /
• v.9 no.4
• /
• pp.337-357
• /
• 2022

The objective of this study is to present a data-driven machine learning (ML) framework for predicting ultimate shear strength and failure modes of reinforced concrete ledge beams. Experimental tests were collected on these beams with different loading, geometric and material properties. The database was analyzed using different ML algorithms including decision trees, discriminant analysis, support vector machine, logistic regression, nearest neighbors, naïve bayes, ensemble and artificial neural networks to identify the governing and critical parameters of reinforced concrete ledge beams. The results showed that ML framework can effectively identify the failure mode of these beams either web shear failure, flexural failure or ledge failure. ML framework can also derive equations for predicting the ultimate shear strength for each failure mode. A comparison of the ultimate shear strength of ledge failure was conducted between the experimental results and the results from the proposed equations and the design equations used by international codes. These comparisons indicated that the proposed ML equations predict the ultimate shear strength of reinforced concrete ledge beams better than the design equations of AASHTO LRFD-2020 or PCI-2020.

• Transactions on Electrical and Electronic Materials
• /
• v.7 no.5
• /
• pp.217-223
• /
• 2006

When only partial information is available about equipment failures (installation date and amount, as well as failure and replacement rates), data on sufficiently large number of yearly populations of the components can be combined, and estimation of model parameters may be possible. The parametric models may then be used for forecasting of the system's short term future failure and for formulation of replacement strategies. We employ the Weibull distribution and show how we estimate its parameters from past failure data. Using Monte Carlo simulations, it is possible to assess confidence ranges of the forecasted component performance data.

• Journal of Applied Reliability
• /
• v.14 no.4
• /
• pp.230-235
• /
• 2014

The failure modes of pneumatic directional control valves include leakage, wear of the spool seal, and sticking of the spool. Among them, the main failure mode of the valve is leakage. The leakage is caused by the wear of the spool seal. However, due to the characteristics of the seal material, the leakage rate is fluctuated a lot rather than constantly increased over time. If life analysis is performed using the first time data of leakage failure, predicted life cycles can be different from the real life cycles. This paper predicts life cycles of the pilot pneumatic directional control valve based on the three point moving average which considers the average of the fluctuating leakage rate.