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- 저자 : Kegui Jiang (검색결과 2 건)
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Lei Wang,Chaoqun Teng,Kegui Jiang,Chuang Jiang,Shangjun Zhu 대한토목학회 2022 KSCE JOURNAL OF CIVIL ENGINEERING Vol.26 No.1
Monitoring and predicting mining area subsidence caused by coal mining help effectively control geological disasters. Information regarding small surface deformations can be obtained using a differential interferometric synthetic aperture radar (D-InSAR), which exhibits high monitoring accuracy and can cover large areas; thus, D-InSAR are being applied for mining area settlement monitoring. However, mining areas are prone to large gradient deformations in short durations; obtaining information regarding such deformations is outside the scope of D-InSAR monitoring. To adapt to the characteristics of D-InSAR monitoring, this study selected a Boltzmann function model with a slow boundary convergence rate to address the problem of probabilistic integration methods exhibiting a high boundary convergence rate. The three-dimensional surface deformation of the mining area can be accurately obtained. According to the projection relationship between D-InSAR line of sight (LOS) directional deformation, subsidence, and horizontal movement, a D-InSAR monitoring equation for mining subsidence assisted by Boltzmann is derived. This equation is combined with the shuffled frog leaping algorithm (SFLA) to obtain the parameters to be estimated from the equation. The D-InSAR LOS deformation information of the 13121 working face in the Huainan mining area was obtained from July 14 to October 30, 2019. The proposed method was then used to obtain the predicted parameters of the working face under insufficient mining. The predicted parameters of mining subsidence were calculated to obtain the predicted parameters when it was fully mined. Then, the revised parameters were used to predict the subsidence and horizontal movement of the mining area and compared with the actual leveling observations. The results show that the mean square error of predicted subsidence is 97.1 mm, which is about 3.09% of the maximum subsidence value; the mean square error of predicted horizontal movement is 46.1 mm, which is about 4.1% of the maximum horizontal movement value. The predicted results of the aforementioned method were used to analyze the damage to the buildings above the working face and determine the damage level of the buildings to provide a reference for the demolition and maintenance of the Zhaimiao village.
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Lei Wang,Shangjun Zhu,Chuang Jiang,Jingyu Li,Kegui Jiang,Chaoqun Teng,Tao Wei,Qing-biao Guo 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.11
When 3D laser scanning technology is used to monitor the surface deformation of mining subsidence in mining area, the surface of the working face is covered with a large number of vegetation, and the surface water accumulates above the working face, which makes the point cloud data obtained by the 3D laser scanning difficult to denoise, or even missing. At this time, the conventional 3D laser scanning technology can not obtain the surface deformation field of mining subsidence. Aiming at the above problems, the 3D laser scanning monitoring method for mining subsidence based on the auxiliary for PIM proposed (3DLS-PIM). Firstly, this paper introduces the PIM prediction model. Secondly, the mining subsidence observation equation based on 3DLS-PIM is constructed, and then the prediction parameters of PIM are solved based on quantum particle swarm optimization (QPSO). Finally, according to the PIM and its parameters, the mining subsidence surface deformation basin is predicted and obtain the surface deformation field of mining subsidence. Robust experiments show that QPSO has a certain ability to resist random errors and gross errors. The results of engineering application show that the mining area 3D deformation monitoring method proposed in this paper has certain engineering application value.
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