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SLOPE STABILITY ANALYSIS OF KALLAR-COONOOR HILL ROAD STRETCH OF THE NILGIRIS | Science Publications

机译:尼尔吉里斯州卡拉尔-库努尔山路伸展的边坡稳定性分析科学出版物

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> The stability of slopes is always under severe threats in many parts of Western Ghats, especially in Kallar-Coonoor hill road stretch, causing disruption, loss of human life and economy. To minimize the instability of soil slope in between Kallar-Coonoor, a critical evaluation of roads is required. The stability of slopes depends on the soil shear strength parameters such as Cohesion, Angle of internal friction, Unit weight of soil and Slope geometry. The stability of a slope is measured by its factor of safety using geometric and shear strength parameter based on infinite slopes. In this present study, investigation was carried out at 32 locations in the above said hill road stretch to estimate the factor of safety of the slope determined by Mohr-Coulomb theory based on shear strength parameter calculated from direct shear test which is a conventional procedure for this study. Back Propagation Artificial Neural Network (BP-ANN) Model is used to predict the factor of safety. The input parameters for the (BP-ANN) are chosen as Cohesion, Angle of internal friction, Density and Slope angle and the factor of safety as output. Out of the parameters of 32 locations, the study of BP-ANN is trained with parameters of first 25 locations. Factor of safety was calculated for the remaining 7 locations. The results obtained in BP-ANN method were compared with that of conventional method and observed a good agreement between these two methods. The results obtained from these two methods were also compared with the details of actual field Landslide occurred and indicates 71.4% of conventional method locations matching with the physical occurrences and 85.7% of BP-ANN predicted vulnerable locations match with the physically observed landslide locations.
机译: >在西高止山脉的许多地区,尤其是在Kallar-Coonoor丘陵路段中,斜坡的稳定性始终受到严重威胁,从而造成破坏,人员伤亡和经济损失。为了最大程度地减少Kallar-Coonoor之间的土坡不稳定性,需要对道路进行严格评估。边坡的稳定性取决于土壤抗剪强度参数,例如内聚力,内摩擦角,单位土重和边坡几何形状。使用基于无限边坡的几何和抗剪强度参数,通过安全系数来测量边坡的稳定性。在本研究中,在上述山坡路段的32个位置进行了调查,以基于直接剪切试验计算出的剪切强度参数,通过Mohr-Coulomb理论确定边坡的安全系数,这是常规方法。这项研究。反向传播人工神经网络(BP-ANN)模型用于预测安全系数。 (BP-ANN)的输入参数选择为内聚力,内摩擦角,密度和倾斜角以及安全系数作为输出。在32个位置的参数中,使用前25个位置的参数训练BP-ANN的研究。计算其余7个地点的安全系数。将BP-ANN方法获得的结果与常规方法进行了比较,发现这两种方法之间具有很好的一致性。还将这两种方法获得的结果与实际发生的滑坡发生的细节进行了比较,表明常规方法中有71.4%的位置与实际发生的位置相匹配,而BP-ANN预测的脆弱位置中有85.7%与实际观察到的滑坡位置相匹配。

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