崩塌落石沿直線型斜面的運動特征室內(nèi)試驗研究
[Abstract]:The collapse disasters in the southwest mountainous areas occur frequently, sudden strong, high randomness, often bring huge damage to the roads and houses, and even endanger the lives of the people in the affected areas. In order to adapt to the rapid development of economy, it is often necessary to carry out the prevention and control work of collapse and falling stone, and the movement characteristic of collapse and falling stone is an important index to carry out the protection design. Based on the field investigation of 5 groups of collapses in Dafo Village of Longquanyi Shanquan Town, this paper studies the characteristics of collapse disaster and analyzes the influencing factors of rock falling movement. The instability model of falling rock, the height of collapse source, slope gradient and material composition of slope are used as variables to design and study the characteristics of falling rock movement. The horizontal motion distance, deviation ratio and maximum spring height of these variables to rock fall are analyzed. Maximum velocity of motion, the impact of the landing area. At the same time, based on the experimental data, the prediction models of falling, toppling and sliding are established based on the wavelet neural network, and the distance of rock falling is predicted. The predicted values are converted to the values of the same scale as the collapse site and compared with the actual measurements. Finally, based on the results of laboratory tests, some suggestions for the prevention and treatment of 5 groups of collapse in Dafo Village are put forward. The main contents and results of this paper are as follows: (1) different instability models, height of collapse source and material composition on slope have different effects on the movement characteristics of rock fall. The height of the collapse source is insensitive to the horizontal movement distance, the migration ratio and the maximum spring height; The effect of falling and toppling on the horizontal movement distance, the deviation ratio and the maximum spring height is stronger than that of the sliding type, but it has little relation to the maximum velocity of movement. When the slope is 50-60 擄, the distance of horizontal movement is relatively large, and the maximum is reached when the slope is 50 擄. When the slope is 40-60 擄, it is easy to produce greater lateral migration. When the slope is 40-70 擄, the maximum spring height is positively correlated with the slope, and the maximum velocity of motion is always positively correlated with the slope. Turf slope can effectively reduce the leaping height. (2) different motion modes correspond to different slope critical values. When the slope is 20-40 擄, jumping, sliding, rolling or combined motion between each other occur. When the slope is 40-50 擄, the motion mode is a combination motion between jumping and sliding or between two pairs, and rolling rarely occurs. When the slope is 50-70 擄, pure slippage and pure jump occur, and the combined motion of sliding and jumping rarely occurs, and basically does not roll. (3) based on wavelet neural network, the prediction model of rock drop distance is proposed based on falling model. Toppling prediction model, sliding prediction model, The model can predict the movement distance of falling stone in engineering examples. (4) based on the research results of indoor test, some suggestions for prevention and control of falling stone are put forward: according to the results of experimental research and the location of residential buildings, passive protection lines are set up. Different energy levels and different protective heights of the system, The "man" type retaining wall is designed on the slope surface of the corresponding slope in residential buildings, and the stone falling trough is excavated on the inner side of the highway near the slope. The active protection system is adopted for the slope with large distribution of dangerous rock.
【學(xué)位授予單位】:成都理工大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2017
【分類號】:P642.21
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