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Prediction of Adfreeze Bond Strength Using Artificial Neural Network (인공신경망을 활용한 동착강도 예측)

  • Ko, Sung-Gyu;Shin, Hyu-Soung;Choi, Chang-Ho
    • Journal of the Korean Geotechnical Society
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    • v.27 no.11
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    • pp.71-81
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    • 2011
  • Adfreeze bond strength is a primary design parameter, which determines bearing capacity of pile foundation in frozen ground. It is reported that adfreeze bond strength is influenced by various affecting factors like freezing temperature, confining pressure, characteristics of pile surface, soil type, etc. However, several limited researches have been performed to obtain adfreeze bond strength, for past studies considered only few affecting factors such as freezing temperature and type of pile structures. Therefore, there exists a limitation of estimating the design parameter of pile foundation with various factors in frozen ground. In this study, artificial neural network algorithm was involved to predict adfreeze bond strength with various affecting factors. From past five studies, 137 data for various experimental conditions were collected. It was divided by 100 training data and 37 testing data in random manner. Based on the analysis result, it was found that it is necessary to consider various affecting factors for the prediction of adfreeze bond strength and the prediction with artificial neural network algorithm provides enough reliability. In addition, the result of parametric study showed that temperature and pile type are primary affecting factors for adfreeze bond strength. And it was also shown that vertical stress influences only certain temperature zone, and various soil types and loading speeds might cause the change of evolution trend for adfreeze bond strength.

Effects of Fiber Orientations and Hybrid Ratios on Lubricant Tribological Characteristics of $Al_2O_{3f}/SiC_p$ Reinforced MMCs ($Al_2O_{3f}/SiC_p$ 금속복합재료의 섬유방향과 혼합비가 윤활마모특성에 미치는 영향)

  • Wang, Yi-Qi;Song, Jung-Il
    • Composites Research
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    • v.22 no.5
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    • pp.15-23
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    • 2009
  • The lubricant tribological characteristics of $Al_2O_3$ fiber and SiC particle hybrid metal matrix composites (MMCs) fabricated by squeeze casting method was investigated using a pin-on-disk wear tester. The wear tests of the MMCs were performed according to fiber/particle hybrid ratio in the planar-random (PR) and normal (N) orientations sliding against a counter steel disk at a fixed speed and $25\;kg_f$ loading under different sliding distances and temperatures. The test results showed that the wear behavior of MMCs varied with fiber orientation and hybrid ratio. At room temperature, the lubricant wear behavior of F20P0 unhybrid PR-MMCs was superior to that of N-MMCs while the hybrid composites exhibited the reverse lubricant wear behavior. It was also revealed that the wear resistance of PR-MMCs was superior to that of the N-MMCs due to the joint action of reinforcements and lubricant film between the friction surfaces at an elevated temperature of $100^{\circ}C$ for both fiber only and hybrid cases. In case of $150^{\circ}C$, although the trend of weight loss was similar to that of others, the wear resistance of PR-MMCs was better than that of N-MMCs for hybrid MMCs.