山地变电站的边坡状态检测与稳定性分析

发布时间：2019-01-10 21:15

【摘要】：云南省海拔平均在2000米以上,由于整体地势较高,受限于较为不利的地形和地貌条件,很多变电站要在山地上建设,进行大量的填方挖方构成变电站的边坡。山地变电站边坡失稳会给云南电力系统带来巨大影响,因此有必要对变电站边坡的稳定性进行分析与监测。目前,在土木工程中运用了很多技术和方法来完成边坡结构的安全性监测,并且在不断地改进和优化,以前的大部分监测实例都是采用人工观测或是使用单点的监测仪器来完成,而目前随着科学技术的进步,运用更加精确、更加合理的模型分析和自动化监测来实现。基于以上需求本文开展了山地变电站的边坡状态检测与稳定性分析研究。研究内容主要包含以下几个方面:(1)进行山地变电站边坡的地质结构分析及尺寸测量,运用GeoStudio的Slope/w模块建立仿真分析模型,对边坡的滑面进行针对性搜索得出临界滑裂面,并对临界滑裂面上点进行受力计算,分析最大受力区域,根据仿真结论确定边坡危险滑面位置在二级台阶下方2至6m处。(2)针对山地变电站边坡结构,分析极限平衡条分法的适用性,根据受力的假设条件差异,分别运用Fellenius法、Bishop法、Janbu法进行边坡结构的状态分析,安全系数最小值为1.304,结论得出布设传感器进行监测时主要针对回填土质下级边坡位置,并完成山地变电站边坡监测模型的建立。(3)根据山地变电站边坡的实际应变监测数据,进行边坡的稳定性分析,其最大实际应变值为110με,此应变值处于安全范围以内,判定该边坡为稳定状态。基于RBF神经网络算法设计了具有预测能力的分析模型,将边坡压力预测值与监测值相互结合做出对比分析,分析结果得出压力变化曲线拟合度较高且压力相对误差值小于0.1,变化值较为稳定,从而判定此边坡处于稳定状态。
[Abstract]:The average elevation of Yunnan Province is more than 2000 meters. Due to the relatively high overall topography and limited by the more unfavorable terrain and geomorphological conditions, many substations have to be constructed on the mountain ground, and a large number of fill excavations are carried out to make up the slope of the substation. The slope instability of mountain substation will bring great influence to Yunnan electric power system, so it is necessary to analyze and monitor the slope stability of substation. At present, many techniques and methods have been used in civil engineering to monitor the safety of slope structure, and have been continuously improved and optimized. Most of the previous monitoring examples were accomplished by artificial observation or single point monitoring instrument, but now with the progress of science and technology, more accurate and reasonable model analysis and automatic monitoring are used. Based on the above requirements, this paper carried out the slope state detection and stability analysis of mountain substation. The research contents mainly include the following aspects: (1) the geological structure analysis and size measurement of mountain substation slope are carried out, and the simulation analysis model is established by using the Slope/w module of GeoStudio. The critical slip surface is obtained by searching the slip surface of the slope, and the stress on the critical slip surface is calculated, and the maximum force area is analyzed. According to the simulation conclusion, the dangerous slide surface position of the slope is determined at 2 to 6 m below the secondary step. (2) according to the slope structure of the mountain substation, the applicability of the limit equilibrium strip method is analyzed, and the Fellenius method is used respectively according to the difference of the stress assumption condition. Bishop method and Janbu method are used to analyze the state of slope structure. The minimum safety factor is 1.304. Conclusion: when installing sensors for monitoring, it is mainly aimed at the lower slope position of backfill soil. The monitoring model of mountain substation slope is established. (3) according to the actual strain monitoring data of mountain substation slope, the slope stability is analyzed. The maximum actual strain value is 110 渭蔚, and the strain value is within the safe range. The slope is determined to be stable. Based on the RBF neural network algorithm, a predictive analysis model is designed, and the slope pressure prediction value is combined with the monitoring value to make a comparative analysis. The analysis results show that the curve of pressure variation has higher fitting degree and the relative error of pressure is less than 0.1, and the variation value is relatively stable, which determines that the slope is in a stable state.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM63;TP274

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