Finite Element Method (FEM) - Engineering Assignment Help

Assignment Task

Part 1: Finite Element Method (FEM)
Finite Element Method (FEM) is a numerical (approximate) method for the analysis of continuous and discontinuous systems in engineering, applied science, and mathematics. Currently, FEM is the most widely employed method of analysis in civil, mechanical, and aerospace engineering especially for stress analysis. It is also applied in fluid dynamics, thermal analysis, etc. The main advantage of FEM is that it can be applicable to either continuous (continuum) or discontinuous (frames, networks) systems or to a combination of them, particularly when they are subjected to complex boundary conditions (BCs) where closed form solutions are not available.

Part 2: Dynamics of Structures
In the first part of this project, we used FEM to investigate the mechanical properties of a single component in the electrical system of the high voltage switch. For the purpose of designing and integrating a smart system, in this part of the project, three structures are analyzed for their dynamic properties. The first structure is a one-story 3D braced steel frame, which supports multiple post insulators. The second structure is a typical photovoltaic power plant. Moreover, as an extension of this simple support structure, which can be reasonably modeled in the lateral direction as a single-degree of freedom (SDOF) system, the dynamic properties of Burj Khalifa, which is thought as multi-degree of freedom (MDOF) system, is explored as the third structure.

Part 3: Data-driven Vision-based Structural Health Monitoring
As mentioned in the lectures, vision-based approach is an important and well-studied direction in nowadays structural health monitoring (SHM). The objective of vision-based SHM is to detect vision patterns, e.g., cracking, spalling, and buckling to be able to extract information related to damage, i.e., damage level, damage type, etc., through images and videos. Usually, this work is performed by human or some automated algorithm, but there exist many drawbacks for such approach where tedious and repetitive inspection work for human and inaccurate detection results for automated algorithms exist. In this data explosion epoch, artificial intelligence (AI) and
machine learning (ML) technologies are developing rapidly, especially in applications of deep learning (DL) in computer vision, which made giant progress in recent years.

In addition, the objective of the implementation of ML and DL is to make computers perform labor-intensive repetitive tasks and also learn from past experiences with a stable and highly accurate procedures.
Considering the difficulties in vision-based SHM, which greatly relies on human visual inspection and detection accuracy, it is timely to implement the state-of-the-art DL technologies in visionbased SHM applications and evaluate their potential benefits, especially in detecting health conditions of critical infrastructure such as electrical equipment and systems.

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