Ground Anchores

Object detection is related to computer vision and involves identifying the kinds of objects that have been detected. It is challenging to detect and classify objects. Recent advances in deep learning have allowed it to detect objects more accurately. In the past, there were several methods or tools used: R-CNN, Fast-RCNN, Faster-RCNN, YOLO, SSD, etc. This research focuses on "You Only Look Once" (YOLO) as a type of Convolutional Neural Network. Results will be accurate and timely when tested. So, we analysed YOLOv3's work by using Yolo3-tiny to detect both image and video objects.

It is not a rare practice that retaining structures in BiH are built as piles additionally supported by ground
anchors. These walls are often design in road engineering to support cut slopes and in private sector
for retaining of deep excavations. Designing the piles without anchors usually cannot provide
appropriate safety factor and cannot appropriately reduce displacements, since the piles flexural
capacity is quite limited. In many practical examples it is not possible to perform large diameter piles in
unstable areas (due to heavy equipment), so only small diameter piles (D < 600,0 m) must be used for
remediation. The construction phases for this type of retaining structures were defined in special chapter
of this manual. One of the walls is selected as case study for this manual. The details like construction
sequence, geotechnical investigations, numerical analysis, monitoring results and interpretations along
with the practical construction details are presented in order to get the insight to possibilities of these
structures. On the other side, the shortcomings of these structures are highlighted to draw the attention
to practical engineers that will built these walls in the future.

The task of predicting the performance of deep excavations is challenging due to the existence of many influencing factors.  Strength and deformation parameters of soil, type, stiffness of the support system, and sequence of excavation and installation of support elements are very important factors in studying the performance.  Performance of deep excavation support systems is related to both stability and deformation.  Ground deformation around the excavations can damage surrounding buildings, streets and utilities. Therefore understanding the factors affecting the performance of deep excavations and the ability to predict the behavior of the support system and the associated ground deformations is an important issue for geotechnical engineers.
In this thesis, two case studies with well documented data of field measurements are analyzed numerically.  The analyses are performed using the finite element program “PLAXIS-2D”.  Two material soil models are implemented in the analyses: the hardening soil model and Mohr Coulomb model.  In both case studies analyzed, good agreement between the field measured displacements and the calculated displacement using the hardening soil model is obtained.  Results of analysis showed also that the hardening soil model is superior to Mohr Coulomb model in predicting the displacements in both case studies.
After calibrating the numerical model, a parametric study is performed to investigate the effect of anchor-pretension-force on the resulting deformation for case study (A) and the effect of strut stiffness on the resulting deformation for case study (B).  Results of analysis showed that both factors have important effects on the resulting excavation-support-system deformation.