The Experts below are selected from a list of 13410 Experts worldwide ranked by ideXlab platform
Wen Zhang - One of the best experts on this subject based on the ideXlab platform.
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a new method for three dimensional fracture network modelling for trace data collected in a large Sampling Window
Rock Mechanics and Rock Engineering, 2020Co-Authors: Zhenbang Nie, Jianping Chen, Wen Zhang, Chun Tan, Fengyan Wang, Ying Zhang, Jinsheng QueAbstract:This study presents a new three-dimensional (3D) network modelling method for fractures collected in a large Sampling Window, in which the determination of fracture disc diameter is the critical step. To derive the diameter, the disc radius of the fractures of the investigated exposed rock surface is initially obtained based on the collected trace lengths. Then, the disc radius of the fractures in 3D space is deduced. The determination of the density and orientation of fractures is also included in the study. Subsequently, the 3D fracture networks for each fracture set are generated based on the derived diameter, density and orientation. To verify the rationality of the method, the rock masses downstream of the sluice gate of Datengxia hydropower station are selected as study objects, and a plane with identical orientation to the exposed rock surface is intersected by the network, thereby obtaining the fracture traces in the plane. The statistical characteristics of fracture traces in the plane and those of the exposed rock surface are highly similar. Thus, the proposed method is feasible.
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A 3D Fracture Network Model for the Undisturbed Rock Mass at the Songta Dam Site Based on Small Samples
Rock Mechanics and Rock Engineering, 2015Co-Authors: Xudong Han, Jianping Chen, Qing Wang, Wen ZhangAbstract:List of symbols w Width of the Sampling Window h Height of the Sampling Window ai Dip direction of the ith joint ar Strike of the Sampling Window hi Dip angle of the ith joint di Fracture diameter of the ith joint Wi Weight formula for the ith joint Rfi Corrected relative frequency of the ith joint n Number of joints in a fracture set ei The ith parameter of the supposed probability density function lj The jth trace length of the measured joint of a fracture set v Sample size or number of sectors S Statistic of the Kolmogorov–Smirnov test S Critical value of the Kolmogorov–Smirnov test p Approximate significance level lm Mean trace length rm Standard deviation of the measured trace length distribution ll True mean trace length rl Standard deviation of the true trace length distribution lD Mean joint diameter rD Standard deviation of the joint diameter distribution ðCOV)m Coefficient of variation of the measured trace length distribution EðDÞ The mth moment of the joint diameter, m = 1, 2, 3... EðlÞ The mth moment of the trace length, m = 1, 2, 3... k1i Normal line density of the ith fracture set kVi Fracture density in unit volume of the ith fracture set V Volume of simulated space region
Jinsheng Que - One of the best experts on this subject based on the ideXlab platform.
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a new method for three dimensional fracture network modelling for trace data collected in a large Sampling Window
Rock Mechanics and Rock Engineering, 2020Co-Authors: Zhenbang Nie, Jianping Chen, Wen Zhang, Chun Tan, Fengyan Wang, Ying Zhang, Jinsheng QueAbstract:This study presents a new three-dimensional (3D) network modelling method for fractures collected in a large Sampling Window, in which the determination of fracture disc diameter is the critical step. To derive the diameter, the disc radius of the fractures of the investigated exposed rock surface is initially obtained based on the collected trace lengths. Then, the disc radius of the fractures in 3D space is deduced. The determination of the density and orientation of fractures is also included in the study. Subsequently, the 3D fracture networks for each fracture set are generated based on the derived diameter, density and orientation. To verify the rationality of the method, the rock masses downstream of the sluice gate of Datengxia hydropower station are selected as study objects, and a plane with identical orientation to the exposed rock surface is intersected by the network, thereby obtaining the fracture traces in the plane. The statistical characteristics of fracture traces in the plane and those of the exposed rock surface are highly similar. Thus, the proposed method is feasible.
Jianping Chen - One of the best experts on this subject based on the ideXlab platform.
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a new method for three dimensional fracture network modelling for trace data collected in a large Sampling Window
Rock Mechanics and Rock Engineering, 2020Co-Authors: Zhenbang Nie, Jianping Chen, Wen Zhang, Chun Tan, Fengyan Wang, Ying Zhang, Jinsheng QueAbstract:This study presents a new three-dimensional (3D) network modelling method for fractures collected in a large Sampling Window, in which the determination of fracture disc diameter is the critical step. To derive the diameter, the disc radius of the fractures of the investigated exposed rock surface is initially obtained based on the collected trace lengths. Then, the disc radius of the fractures in 3D space is deduced. The determination of the density and orientation of fractures is also included in the study. Subsequently, the 3D fracture networks for each fracture set are generated based on the derived diameter, density and orientation. To verify the rationality of the method, the rock masses downstream of the sluice gate of Datengxia hydropower station are selected as study objects, and a plane with identical orientation to the exposed rock surface is intersected by the network, thereby obtaining the fracture traces in the plane. The statistical characteristics of fracture traces in the plane and those of the exposed rock surface are highly similar. Thus, the proposed method is feasible.
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A 3D Fracture Network Model for the Undisturbed Rock Mass at the Songta Dam Site Based on Small Samples
Rock Mechanics and Rock Engineering, 2015Co-Authors: Xudong Han, Jianping Chen, Qing Wang, Wen ZhangAbstract:List of symbols w Width of the Sampling Window h Height of the Sampling Window ai Dip direction of the ith joint ar Strike of the Sampling Window hi Dip angle of the ith joint di Fracture diameter of the ith joint Wi Weight formula for the ith joint Rfi Corrected relative frequency of the ith joint n Number of joints in a fracture set ei The ith parameter of the supposed probability density function lj The jth trace length of the measured joint of a fracture set v Sample size or number of sectors S Statistic of the Kolmogorov–Smirnov test S Critical value of the Kolmogorov–Smirnov test p Approximate significance level lm Mean trace length rm Standard deviation of the measured trace length distribution ll True mean trace length rl Standard deviation of the true trace length distribution lD Mean joint diameter rD Standard deviation of the joint diameter distribution ðCOV)m Coefficient of variation of the measured trace length distribution EðDÞ The mth moment of the joint diameter, m = 1, 2, 3... EðlÞ The mth moment of the trace length, m = 1, 2, 3... k1i Normal line density of the ith fracture set kVi Fracture density in unit volume of the ith fracture set V Volume of simulated space region
Jingyan Song - One of the best experts on this subject based on the ideXlab platform.
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a novel systematic error compensation algorithm based on least squares support vector regression for star sensor image centroid estimation
Sensors, 2011Co-Authors: Jun Yang, Bin Liang, Tao Zhang, Jingyan SongAbstract:The star centroid estimation is the most important operation, which directly affects the precision of attitude determination for star sensors. This paper presents a theoretical study of the systematic error introduced by the star centroid estimation algorithm. The systematic error is analyzed through a frequency domain approach and numerical simulations. It is shown that the systematic error consists of the approximation error and truncation error which resulted from the discretization approximation and Sampling Window limitations, respectively. A criterion for choosing the size of the Sampling Window to reduce the truncation error is given in this paper. The systematic error can be evaluated as a function of the actual star centroid positions under different Gaussian widths of star intensity distribution. In order to eliminate the systematic error, a novel compensation algorithm based on the least squares support vector regression (LSSVR) with Radial Basis Function (RBF) kernel is proposed. Simulation results show that when the compensation algorithm is applied to the 5-pixel star Sampling Window, the accuracy of star centroid estimation is improved from 0.06 to 6 × 10−5 pixels.
Longgang Tian - One of the best experts on this subject based on the ideXlab platform.
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estimation of fracture orientation distributions from a Sampling Window based on geometric probabilistic method
Rock Mechanics and Rock Engineering, 2021Co-Authors: Qi Zhang, Xiaojun Wang, Longgang TianAbstract:Accurate orientation distributions are crucial to generating a reliable discrete fracture network (DFN) model for rock mass, while conventional one-dimensional (1D) and two-dimensional (2D) observation data have significant Sampling bias. The study proposes a complete analytical method for estimating the orientation distributions of three-dimensional (3D) fractures in rock mass in conjunction with trace statistics in a Sampling Window, which is suitable for most continuous distributions by reducing the Sampling bias. Traces are divided into three categories to derive the geometric probabilistic relationships between 2D trace statistics and 3D fracture orientation distributions. The moment estimation, number estimation, and normalization error functions are derived, and the distribution parameters are determined by minimizing the total error function. The proposed method is compared with the Terzaghi family methods and validated by multiple sets of stochastic fracture networks with different orientation distributions and Sampling Windows generated by the Monte Carlo method. The results indicate that the estimated continuous orientation distributions subjected to the error functions from a large single Sampling Window are well matched with the true distributions after removing the number estimation error functions of trace samples fewer than 20. Daxiagu tunnel is selected as a case study and the distributions estimated by the proposed method are more coincident with the field observation than those fitted by the orientations of the rock outcrops on the excavation face.
