LAUSR

The monitoring and prediction of slope displacement plays a vital role in slope stability analysis. The displacement changes influences the stability of the slope. This paper described an approach to monitoring and predicting the variation of slope displacement. Firstly, The target slope was photographed from multiple angles by unmanned aerial vehicle (UAV) gimbal controller. Then an image algorithm of visual motion is applied to reconstruct the point cloud of the slope, and to get the digital elevation model (DEM) of the slope. The DEM model was used to calculate the displacement of the monitoring area. The Displacement information measured by UAV photogrammetry will be divided into training sample set and the prediction sample set. Using the AFSA-Elman algorithm neural network, the displacement sequences were trained and predict the variation of the displacement sequence. Compared with the on-site measurement results and existing Elman network, the result demonstrated that the UAV measurement technique have a high efficiency in monitoring the displacement at each measured point of the slope. And the AFSA-Elman network was proved a higher precision and better convergence comparing with the traditional Elman network, which was suitable for the predict the displacement of the key measuring point in the slope.