## 3 Discussion of the Results
The 3D or 4D simulation, in comparison with the conventional management techniques, allow global execution strategies [53] and allows one to determine the different levels of risks in heights and observe the overlaps of the stockpiles and their interferences. Therefore, a digital elevation map can be a movement control tool that allows security operators to determine the scenario of \"Static Elements\" and the free space to occupy in the lines of operations or spaces free of obstacles. From this digital elevation map, a graph can be illustrated with the percentages of the number of points per height (Figure 11), where the range of height of the operation plan and the activities planned and recorded in phase 1 will be represented on the x-axis. 62% of the plane (x, y) is free of obstacles, and this proportion decreases from 0.25 m to 18% of the total space occupied. From 70 cm in height, the map determines percentages much lower than 5%. This translates into an analysis of the histogram (Figure 12), where the majority of the points are below \(-1.00\) m, representing spaces free of obstacles, hence the importance of showing the histogram as a basis for analyzing the results. In addition to all the procedures analyzed, according to Equation (2), a graph of the overlaps of static elements has been determined (Figure 19). Stage marks the coordinates in a \(\Pi\) (x, y) plane, the exact position of static elements, and the degree of overlap. With them, it is possible to insert the cloud of points in a BIM modelling environment, build stockpiling scenarios from the graph, and detect collisions between certain spaces, advancing planning and organization aspects. Closest point interference occurs on the stage between Stockpiles Steel and Stockpiles Wood. The study of stockpiles in BIM could determine, with real elements, whether the BIM software itself has the properties of establishing object collisions. From the digital elevation model, a rasterization of the point cloud is performed in phase 4 through the CloudCompare software. The first phase of point cloud rasterization is to establish a grid of square cells in the (x, y) plane. There are several raster algorithms, some based on pixels and others based on points. [PERSON] et al. [59], based on the citation of [60], state that pixel-based LiDAR classification methods are less accurate than point-based methods. Points that are established in a 3D model from pixels lose the original information inherent in the points. Among the algorithms that directly apply the LiDAR point cloud are C2C and M3C2 in the author's comparison study. From the rasterization, a profile contour graph is obtained in Figure 20, where it is possible to determine different profiles that generate the degree of security risk intervention in phase 4. The elevation value of each of the DEM profiles defines the location of the static and dynamic elements of the model. This value of the morphometric parameter of the building and the scene to be represented characterizes the morphometry of the activities. In the raster image, it can be determined that the warm colors indicate height values of the work activities, and the cold colors point to range levels of basement plans. Phase 4 is evaluated through the installation of the wall formwork, and for this, a mobile crane is used. The digital model of elevation represents each of the movements of the crane, which allows the planning of activities related to the crane. In this analysis process, the profile of the contour graph, in blue, determines the height of the crane and its movements. The analysis database in the proposed framework contains detailed information on the scenarios of activities that can generate risks in construction. Accurate 3D models from LiDAR systems enable profile-associated simulation. This information can be integrated into BIM platforms through schematics and metadata units for a record while modeling the different construction phases is carried out.