Research On Key Technologies Of Intelligent Monitoring Of Building Deformation In Mining Area Based On Terrestrial Laser Scanning Technology

After the underground coal seam is mined,it is bound to cause the rock strata in the mining area to move,cause serious damage to the surface buildings,and seriously threaten the production and living safety of the surrounding areas.Therefore,it is of great significance to monitor the deformation of buildings in the mining area.In this context,this paper is based on ground laser scanning technology from three aspects:(1)deformation monitoring of mining area buildings,(2)mining damage evaluation of buildings,and(3)the development and implementation of relevant software.The following conclusions are obtained:(1)A TLS automatic monitoring method of building deformation in mining area based on feature points is proposed.This method takes the corner points of doors and windows on the wall as the feature points,and can extract the wall deformation in the mining area with only a small amount of manual intervention.Firstly,the scanned data is preprocessed,including 2D boundary point cloud acquisition and denoising(using the slope filtering proposed in this paper).Then,the feature points are extracted,which is divided into three steps: boundary line splitting,seed feature point clustering and feature point judgment.Finally,the 3D coordinates of feature points and the relationship between feature points are established to calculate the deformation value.(2)A mining damage assessment method combining bf-ektf and TLS is proposed.This method is based on TLS point cloud data in a small range,makes full use of point,line / column features to extract deformation,and obtains large-scale deformation combined with bpm-ektf mining subsidence model.Firstly,based on the features of point and line / column,the discrete deformation value is extracted from the small-scale scanning data.Then,the deformation is described by bpm-ektf model,and the wolf pack algorithm is used to obtain the model parameters.Finally,the model parameters and large-scale regional coordinates are brought into the model to obtain large-scale deformation values.Moreover,this method includes a construction method of static model and time function.(3)Aiming at swarm intelligence algorithm: the wolf pack algorithm is improved.The ability of wolf pack algorithm to solve nonlinear model parameters is improved through secondary walk and mutation behavior.The parameter estimation and software implementation of mining subsidence nonlinear model based on swarm intelligence algorithm are realized.The software takes the probability integral method as the mining subsidence model,summarizes the commonly used swarm intelligence algorithm and the improved swarm intelligence algorithm,introduces them into the parameter estimation of the mining subsidence nonlinear model,and compiles the parameter estimation,three-dimensional mapping,result report generation and so on.Figure [48] Table [8] Reference [82]...