Research On Slope Structure Monitoring And Stability Gray Model Based On Fiber Bragg Grating Strain Pile

The instability of slope often leads to the destruction of building, bridges, etc. The real-time monitoring of the slope structure and prediction of the instability can provide a basis for the slope protection. Traditional slope monitoring techniques cannot meet the requirements of working in the high-magnetic environment and real-time online monitoring because of the poor real-time performance, weak anti-interference ability, etc. The Fiber Bragg Grating (FBG) sensors have been widely used in structure monitoring due to its high precision, low loss, long life, good real-time performance, anti-electromagnetic interference, etc. By injecting strain piles the FBG sensors are deployed in the internal part of slope in order to get the strain values from different depths, so that the stability of the slope structure can be monitored. Considering the above, the slope structure monitoring and stability prediction based on FBG strain piles are studied in this paper. The main content of the study is listed as below:(1) The principle of FBG sensors is studied. For the deployment of strain piles are usually implemented by pouring mortar, a new FBG sensor with a self-tightening lock and a flange is invented. The material selection, machining, instalment and test of the new FBG sensors are all carried out in the project. The test results show that the sensitivity is 0.52pm/ue, the linearity is 3.71%FS and the repeatability is 7.43%FS.(2) Based on the real conditions of the slopes around the mountain substation, a finite element model is built and the strain of the inner part of the slope is analysed by the strength reduction method. Taking the area where the strain is concentrated as the key monitoring point, the project plan is made. Five strain piles are installed at the second step of the slope at depth of 19m,22m,17m, 11m and 19m respectively. A fiber sensor network is set up for the slope monitoring.(3) The stability of the slope is analysed according to the monitoring data. A grey model GM(1,1) is built to predict the slope conditions. The evaluation of the model GM(1,1) shows that the average relative error is 0.00364%. The grey model GM(1,1) can make short-term prediction for the stability of the slope.