جستجوی مقالات مرتبط با کلیدواژه « 3D Model » در نشریات گروه « عمران »

Summary Gravity flow in caving underground mining is a key parameter that effects on dilution and ore recovery. In order to have a successful operation in caving mining, it is necessary to evaluate and estimate the gravity flowability of ore and waste. From past to now, several researchers focused on study of gravity flow by different methods like mathematical, physical and numerical methods. In this study, the gravity flowability of ore and waste in caved zone of Sechahoon Anomaly No. XII was estimated by physical modeling. Then by using DEM modeling, a model based on physical model was created to use for future studies. Introduction In recent studies, sublevel caving method was selected for Sechahoon Anomaly No. XII. In sublevel caving method, we encounter with key parameters like cavability, gravity flow, blastability and subsidence. Therefore, gravity flow study is an important parameter that should be estimated. Gravity flow is studied by several researchers from past to now. These studies carried out by different methods like mathematical, physical and numerical methods. Methodology and Approaches In this study, the physical method was selected for gravity flow modeling. Therefore, a model was designed by a 1:10 scaled, tunnel width, ring height, burden and tunnel height equal to 50, 150, 20 and 40 cm, respectively. The model was filled by iron ore and waste that have a size distribution between 0 to 25 mm. Results and Conclusions The physical modeling results shows that: • Recovery of iron ore is equal to 63 percent. • The angle of repose of iron ore and waste are 43 and 38 degree, respectively. • The shape of gravity flow is similar to common model that presents the validity of model. • Digging depth is equal to 11 cm. • Based on Kvapil suggestion, 48 percent of ore was classified as class A and 20 percent as class B. By using the results of physical modeling, a numerical model is created and validated with physical modeling results in order to avoid from physical modeling problems and use for future studies that are useful in gravity flow studies.

Nowadays the three-dimensional presentation of real world features is very important and useful, and attracted researchers in various branches such as photogrammetry and geographic information systems and those interested in three-dimensional reconstruction of the building. Buildings are the most important part of a three dimensional model of a city, therefore extraction and modeling buildings of remote sensing data are important steps to build a urban digital model.
Although many efforts to reconstruct the three-dimensional building of LiDAR data have been made by researchers in recent years, but challenges still exist in this area, especially in urban areas. In previous studies, dense vegetation and tall trees in the vicinity of the buildings cause to difficulty in the building extraction process and reduction in the accuracy of the modeling results. The aim of this article is extraction and reconstruction of buildings by using LiDAR point clouds in urban areas with high vegetation. In this study, factors such as the LiDAR return pulse, the height of points and area of the region is used to separate the non-structural parts. Ground points in segment-based method by changing the size segment in each iteration by mean and standard deviation of the height of points in any segment extracted. The vegetation points are extracted and identified using LIDAR return pulse and a new method called "three-dimensional imaging of points on two-dimensional surfaces ". The projection process is done in the planes of XY, XZ and YZ. Using Illustration of points and changing the angle of view makes the point clouds be evaluated in different directions. Region expanding algorithm and length constraints imposed in different planes has an important role in the separation of dense vegetation. The modeling of building is done by using break lines and important vertices of the building roof in layers of roof height. Extraction of building edge points and height layers of roof is done separately in each building. This points are isolated by height analysis of the roof points. In the line approximations grouping the points in each height layer, line fitting and adjustment of line directions are factors that caused the break lines and points of the building roof to be correctly created. In roof modeling, the basic structure of the roof is modeled and then the parts on the roof are added to the model. The overall structure of the roof is made by roof vertexes and normal vector of generated planes. At the end, by calculating the point’s distances from roof plane, the roof parts are identified and the model of this components are added to the roof. The proposed method is evaluated on LiDAR point clouds in an area of the Stuttgart, Germany, with a density of 4 points per square meter.
The accuracy of the proposed method is evaluated by visual interpretation and quantitative comparisons with information extracted by a human operator. The accuracy of proposed method is about 96 percent in extracting building points and modeling error at the corner of the building is approximately 44 cm. Overall, the results represent the success of the proposed method in extracting and modeling of buildings in areas with dense vegetation.

In the present research, a 3D numerical model of the sediment grain movement has been developed based on the Eulerian-Lagrangian perspective. The forces which act on sediment grains are non-linear drag force, the shear lift force, the rotational lift (Magnus) force, the buoyancy force, the added mass force, the Basset history force and torque. Second-order nonlinear ordinary differential equations have been used to calculate linear and angular velocity vectors and also the position of sediment grains. Two parts including turbulent fluctuations in 3D space and a stochastic bed-particle collision model have been considered in the modeling procedure. The verification of the developed model has been examined for two important issues including the minimum number of jumps for statistical convergence and the appropriate value of the time step. The validation of the model has been performed using reliable experimental data in terms of the saltation height, length and streamwise particle velocity for the ranges of fine sand to coarse gravel. To evaluate the qualification of the conceptual model, the effects of the elimination of some forces and turbulent fluctuations, where do not exist consensus on the use of these factors in the previous researches, have been studied. The results indicated that the developed model can be considered as a numerical lab to study different aspects of the sediment transport in the prediction step, which will be performed in the part B of this series.

In recent years, the techniques of surface representation have been changed and three-dimensional models have been replaced with two-dimensional maps. Airborne laser scanner as a powerful system has been known in remote sensing as a valuable source for 3D data acquisition from the Earth’s surface which can mainly be employed for 3D reconstruction and modeling. 3D reconstruction of buildings as an important element of 3D city models, based on LiDAR point clouds has been considered in this study. A new non-parametric method is proposed for generation of 3D model of buildings with flat and tilted roof. The approach comprises of four steps for 3D building reconstruction as: (A) Simultaneous building extraction and segmentation, (B) Edge detection, (C) Line approximation, and (D) 3D modeling. In step (A) a multi-agent method is proposed for extraction of buildings from LiDAR point clouds and segmentation of roof points at the same time. In this method five criteria such as height values, number of returned pulses, length, normal vector direction based on Constrained Delaunay Triangulation, and area are utilized. Next, in step (B) the edge points of roof segments are detected. Points of triangles having no neighboring triangles are extracted as primary edge points. In the extraction process, noises, external objects, and tree points on the roofs are eliminated. It is an advantage of the proposed method, however it leads to create the undesired edge points. There is the same problem regarding to segments which contain overlap with each other (like flat building). These undesired edge points as internal points are known and must be removed. In this paper, a method named “Grid Erosion” is employed for removing these internal points and therefore finding real edge points. After detecting the final edge points, a RANSAC-based algorithm is employed to approximate building lines in step (C). RANSAC is a powerful technique in line fitting and in comparison with general least square method, especially with noisy data, it provides robust results. In order to reduce the sensitivity of RANSAC to select parameters and no need for heavy post-processing, edge points are grouped by considering the angle between two consecutive connecting convex points. After classification of edge points, a RANSAC algorithm is separately applied on each classified edge-points group to produce primary lines. The regularization constraints should be applied on primary lines to generate the final lines. Finally, by modeling of the roofs and walls, 3D buildings model is reconstructed in step (D). The proposed method has been applied on the LiDAR data over the Vaihingen city, Germany. Building roof model is manually digitized from LiDAR point clouds and compared with building roof models that reconstructed using proposed method. In model reconstruction, the dominant errors are close to 30 cm which is calculated in horizontal distance. The main advantage of this method is its capability for segmentation and reconstruction of flat buildings containing parallel roof structures even with very small height differences (e.g. 10 cm). The results of both visual and quantitative assessments indicate that the proposed method could successfully extract the buildings from LiDAR data and generate the building models.

Today, 3D models of urban as one of the most important information are needed in many engineering applications such as urban planning, virtual tourism, navigation and emergency response. In recent years, by introducing the LiDAR system and using the 3D geo-referenced data, different methods have been proposed for 3D building modeling. Due to direct accessibility of 3D coordinate of LiDAR points, planar patch’s mathematical equations of building roofs could be determined accurately. However, extracting the edges and boundary of buildings with high accuracy from LiDAR points is a difficult task and will not be always precise. In new aerial laser scanning systems, in addition to using laser to adopt 3D point’s cloud of surface features, equipped by high resolutions small format RGB camera. So besides spatial data of features, RGB and spectral information are also available. In this research a method based on fusion of LiDAR point cloud and aerial image data sources has been proposed. In this study, firstly by using 2D plane (building footprint), points located inside polygon of each building are extracted from the overall scatter, individually. In the next step, the mean shift clustering algorithm applied to the points of different buildings in the feature space. Finally the segmentation stage ended with the separation of parallel and coplanar segments. Then using the adjacency matrix, adjacent segments are intersected and inner vertices are determined. In the other space, the region of any building cropped in the image space and the mean shift algorithm applied to it. Then, the lines of roof’s outline edge extracted bythe Hough transform algorithm and the points obtained from the intersection ofthese lines transformed to the ground space. Finally, by integration of structuralpoints of intersected adjacent facets and the transformed points from image space, reconstruction performed. In order to evaluate the efficiency of proposed method, buildings with different shapes and different level of complexity selected and the results of the 3D model reconstruction evaluated. The results showed credible efficiency of method for different buildings.