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Çѱ¹Áö¹Ý°øÇÐȸ / v.16, no.1, 2000³â, pp.75-81
Àΰø½Å°æÈ¸·Î¸ÁÀ» ÀÌ¿ëÇÑ ¾Ð¹ÐÀÀ·Âºñ¿¡ µû¸¥ Á¤±Ô¾Ð¹ÐÁ¡ÅäÀÇ ºñ¹è¼öÀü´Ü°­µµ ¿¹Ãø
( Prediction of Undrained Shear Strength of Normally Consolidated Clay with Varying Consolidation Pressure Ratios Using Artificial Neural Networks )
ÀÌÀ±±Ô;À±¿©¿ø;°­º´Èñ; ¾Èµ¿Á¤º¸´ëÇÐ Åä¸ñ°ú;ÀÎÇÏ´ëÇб³ °ø°ú´ëÇÐ Åä¸ñ°øÇаú;ÀÎÇÏ´ëÇб³ °ø°ú´ëÇÐ Åä¸ñ°øÇаú;
 
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ÀϹÝÀûÀ¸·Î ÀÚ¿¬»óÅÂÀÇ ÈëÀº À̹漺À» ³ªÅ¸³»¸ç, ÀÌ·¯ÇÑ ÈëÀÇ À̹漺ÀÌ ÀÀ·Â-º¯Çü·ü °Åµ¿¿¡ ¹ÌÄ¡´Â ¿µÇâÀº ¸Å¿ì Å©´Ù. µû¶ó¼­ º» ¿¬±¸¿¡¼­´Â Àΰø½Å°æÈ¸·Î¸Á ¸ðµ¨À» ÀÌ¿ëÇÏ¿© ¾Ð¹ÐÀÀ·Âºñ º¯È­¿¡ µû¸¥ Á¤±Ô¾Ð¹ÐÁ¡ÅäÀÇ ÀÀ·Â-º¯Çü·ü °Åµ¿À» ¸ðµ¨¸µÇÏ°í ºñ¹è¼öÀü´Ü°­µµ¸¦ ¿¹ÃøÇÏ¿© º¸¾Ò´Ù. À̶§ »ç¿ëµÈ ½Å°æÈ¸·Î¸ÁÀº ÀϹÝÈ­µÈ µ¨Å¸±ÔÄ¢À¸·Îµµ ºÒ¸®¿ì´Â ¿ÀÂ÷¿ªÀüÆÄ ÇнÀ ¾Ë°í¸®ÁòÀ» ÀÌ¿ëÇÑ ´ÙÃþ½Å°æÈ¸·Î¸ÁÀÌ´Ù. ½Å°æÈ¸·Î¸ÁÀÇ ÇнÀÀº ÀΰøÅðÀû Á¡Åä½Ã·á¸¦ ÀÌ¿ë, ¿¬Á÷¾Ð¹ÐÀÀ·Â°ú ¾Ð¹ÐÀÀ·Âºñ¸¦ ´Ù¸£°Ô Á¤±Ô¾Ð¹Ð½ÃŲÈÄ ºñ¹è¼öÀü´Ü½ÃÇèÀ» ½Ç½ÃÇÏ¿© ¾ò¾îÁø ½ÃÇè °á°ú¸¦ ÀÌ¿ëÇÏ¿´°í, ÇнÀµÈ ½Å°æÈ¸·Î¸ÁÀ» ÀÌ¿ëÇÏ¿© ÇнÀ½Ã Á¦¿ÜµÇ¾ú´ø ¾Ð¹ÐÀÀ·Âºñ »óÅ¿¡¼­ÀÇ ºñ¹è¼öÀü´Ü°­µµ¸¦ Ãß·ÐÇÏ¿© º» °á°ú ¿¹ÃøÄ¡¿Í ½ÇÃøÄ¡°¡ Àß ÀÏÄ¡ÇÏ¿´´Ù. °ËÅä°á°ú ½ÇÃøÄ¡¿Í Ãß·ÐÄ¡ »çÀÌ¿¡´Â °áÁ¤°è¼ö($r^2$) 0.973 ÀÌ»óÀÇ ³ôÀº »ó°ü°ü°è°¡ ÀÖÀ½À» È®ÀÎÇÏ¿´´Ù. µû¶ó¼­, º» ¿¬±¸°á°ú´Â Á¡ÅäÀÇ ºñ¹è¼öÀü´Ü°­µµ¸¦ ¿¹ÃøÇÔ¿¡ À־ Àΰø½Å°æÈ¸·Î¸Á¸ðµ¨ÀÇ Àû¿ë °¡´É¼ºÀ» º¸¿©ÁÖ¾ú´Ù.
The anisotropy of soils has an important effect on stress-strain behavior. In this study, an attempt has been made to implement artificial neural network model for modeling the stress-strain relationship and predicting the undrained shear strength of normally consolidated clay with varying consolidation pressure ratios. The multi-layer neural network model, adopted in this study, utilizes the error back-propagation loaming algorithm. The artificial neural networks use the results of undrained triaxial test with various consolidation pressure ratios and different effective vertical consolidation pressure fur learning and testing data. After learning from a set of actual laboratory testing data, the neural network model predictions of the undrained shear strength of the normally consolidated clay are found to agree well with actual measurements. The predicted values by the artificial neural network model have a determination coefficient$(r^2)$ above 0.973 compared with the measured data. Therefore, this results show a positive potential for the applications of well-trained neural network model in predicting the undrained shear strength of cohesive soils.
 
Ű¿öµå
Artificial neural networks;Anisotropy;Consolidation pressure ratio;Undrained shear strength;Normally consolidated clay.;
 
Çѱ¹Áö¹Ý°øÇÐȸ³í¹®Áý / v.16, no.1, 2000³â, pp.75-81
Çѱ¹Áö¹Ý°øÇÐȸ
ISSN : 1229-2427
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO200011921750051)
¾ð¾î : Çѱ¹¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
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