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In a previous study, a series of 9 model tests performed on embankment models resting on soft clay treated with and without ordinary stone columns (OSC), encased stone columns (ESC), and with horizontal layer of geogrid at interface and at 0·1 h height has been carried out to investigate the behaviour of piled (stone columned) and geogrid reinforced embankments. In this paper, these tests are simulated numerically by the finite element method using the program Geostudio (SIGMA/W). Firstly, reanalysis was done of experimental work of embankment models constructed on soft clay treated with OSC and ESC. Secondly, embankment models reinforced by a horizontal layer resting on soft clay strengthened with OSC and ESC were analyzed. A horizontal layer of geogrid is embedded at different levels of embankment height. It was concluded that in the embankment models constructed on soft clay treated with stone columns at a spacing s = 2·5 d with length to diameter ratio L/d ratio = 8, the maximum bearing improvement ratio (qt/qunt) equals 1·33 for the ordinary stone columns and 1·59 for the encased stone columns and about 1·13 and 1·23 when one layer of geogrid is embedded at interface or at 0·1 h height, respectively.

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Occlusion awareness is one of the most challenging problems in several fields such as multimedia, remote sensing, computer vision, and computer graphics. Realistic interaction applications are suffering from dealing with occlusion and collision problems in a dynamic environment. Creating dense 3D reconstruction methods is the best solution to solve this issue. However, these methods have poor performance in practical applications due to the absence of accurate depth, camera pose, and object motion.This paper proposes a new framework that builds a full 3D model reconstruction that overcomes the occlusion problem in a complex dynamic scene without using sensors’ data. Popular devices such as a monocular camera are used to generate a suitable model for video streaming applications. The main objective is to create a smooth and accurate 3D point-cloud for a dynamic environment using cumulative information of a sequence of RGB video frames. The framework is composed of two main phases. The first uses an unsupervised learning technique to predict scene depth, camera pose, and objects’ motion from RGB monocular videos. The second generates a frame-wise point cloud fusion to reconstruct a 3D model based on a video frame sequence. Several evaluation metrics are measured: Localization error, RMSE, and fitness between ground truth (KITTI’s sparse LiDAR points) and predicted point-cloud. Moreover, we compared the framework with different widely used state-of-the-art evaluation methods such as MRE and Chamfer Distance. Experimental results showed that the proposed framework surpassed the other methods and proved to be a powerful candidate in 3D model reconstruction.

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