Digital Engineering

Abstract

In this hands-on computer course, based on Python Jupyter notebooks, you will be introduced and explore combined learning of programming and task-solving of civil-engineering relevant problems. By addressing topics where engineers rely on computers and numerics, you will learn about solution strategies and how to put them into practice by setting up solution “pipelines”.

Objective

After the completion of the Digital Engineering course, students … • are able to work with and to set-up own interactive Python notebooks for their further studies. • are able to apply Python by using the functionality of the most popular modules and • can overcome implementation and programming language issues to solve simple civil engineering problems. • are able to handle and analyze large datasets and generate high-quality plots. • are able to work with image data for problem analysis. • get a first insight into important applied problems such as simple equilibrium, dynamic, or optimizations problems and • are able to explain problem specific solution strategies in simple terms and know about their limitations. • are able to combine building-blocks from the different solution- pipelines to address new questions.

Content

The course combines lectures and exercises in a hands-on approach, where students simultaneously work with and on the notebook material presented. Based on the knowledge you gained in the proceeding "Programming for Civil Engineers" course on reading, completing, checking and developing code patterns in Matlab and Python, you will develop a toolset that is problem-solving oriented, based on the most popular Python modules. You will also consolidate Python as your standard programming language for your further studies. All exercise examples are without exception taken from civil engineering practice of various thematic fields. The lecture is segmented in total into 8 main blocks (1L=45min): 1. Hands-on Python (4L+4Ex): Reminding you about Python, and introducing the modules NumPy, SciPy, and MatplotLib. 2. Python-I/O and big data (2L+2Ex): Handling huge datasets with Pandas: viewing, processing, filtering and analysing data. 3. Graphical visualization (2L+2Ex): Generating high-quality plots and figures. 4. Graphical statics: (4L+4Ex): Solving structural problems in setting up solution pipelines based on graphical statics. 5. Image processing and analysis (2L+2Ex): Using images and image sequences for the analysis of engineering problems. 6. Equilibrium solutions (2L+2Ex): Static equilibrium solutions of simple systems composed of multiple parts. 7. Dynamics solutions (4L+4Ex): Getting the time evolution and dynamic behavior of simple systems composed of multiple parts. 8. Optimizing systems (4L+4Ex): Strategies to improve and develop optimized systems by using numeric solutions.

Resources