崩塌落石沿直线型斜面的运动特征室内试验研究

发布时间：2019-02-16 06:30

【摘要】：西南等山地的崩塌灾害发生频繁、突发性强、随机性高,经常给影响范围内的道路、房屋带来巨大破坏,甚至危及影响范围内的人民生命安全。为了适应经济的飞速发展,常常需要开展崩塌落石防治工作,而崩塌落石的运动特征又是进行落实防护设计的重要指标。本文通过对龙泉驿山泉镇大佛村5组崩塌的实地调研,研究了崩塌灾害的特征,分析了落石运动特征的影响因素。将落石的失稳模式、崩塌源高度、斜坡坡度、坡面物质成分作为变量设计研究落石运动特征的室内试验,分析了这些变量对落石的水平运动距离、偏移比、最大弹跳高度、最大运动速度、落点区域的影响。同时根据实验数据基于小波神经网络建立坠落式、倾倒式、滑塌式预测模型,预测落石的运移距离,将预测值转化成与崩塌现场同一尺度的值后与实际量测值比较。最终根据室内试验的研究结果对大佛村5组崩塌提出了一些防治建议。本文主要研究内容及成果如下:(1)不同失稳模式、崩塌源高度、坡度、坡面物质成分对落石的运动特征影响各不相同。崩塌源的高度对落石水平运动距离、偏移比、最大弹跳高度不敏感;坠落式和倾倒式对落石水平运动距离、偏移比、最大弹跳高度的影响均强于滑塌式,但与最大运动速度关系不大;当坡度为50-60°时,落石的水平运动距离相对较大,当坡度为50°时达到最大。当坡度为40-60°时易产生更大的横向偏移,当坡度在40-70°内落石的最大弹跳高度与坡度正相关,最大运动速度与坡度始终正相关。草皮坡面能有效减小落石的弹跳高度。(2)不同的运动方式对应不同的坡度临界值。坡度为20-40°时,发生跳跃、滑动、滚动或相互之间的组合运动。坡度为40-50°时,运动方式表现为跳跃和滑动或两两之间的组合运动,滚动极少发生。坡度为50-70°时,发生纯滑动和纯跳跃,滑动+跳跃的组合运动很少发生,基本不滚动。(3)基于小波神经网络提出了落石运移距离预测基于坠落式预测模型、倾倒式预测模型、滑塌式预测模型,该模型能较好预测工程实例中落石的运移距离。(4)基于室内试验的研究结果提出了一些落石防治的建议:根据试验研究结果和民居分布位置设置被动防护系统的不同能级和不同防护高度,在民居对应边坡坡面上设计“人”字型挡墙,在公路靠近边坡的内侧开挖落石槽,对于大块危岩分布的边坡局部采用主动防护系统。
[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 掳, the distance of horizontal movement is relatively large, and the maximum is reached when the slope is 50 掳. When the slope is 40-60 掳, it is easy to produce greater lateral migration. When the slope is 40-70 掳, 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 掳, jumping, sliding, rolling or combined motion between each other occur. When the slope is 40-50 掳, 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 掳, 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.
【学位授予单位】：成都理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P642.21

【参考文献】