Exploring Python’s Computer Algebra System for Effective training in Structural Analysis

Abstract

This paper explores the benefits of using Python’s Computer Algebra System (CAS) to provide effective training in structural analysis. The authors argue that by introducing a comprehensive and flexible platform for performing symbolic manipulation of equations and equations systems, CAS will allow students to have greater understanding of structural analysis principles. The paper also discusses how this system can provide computer-assisted teaching tools to facilitate learning in the classroom. Structural analysis is a challenging area to teach, as it requires intimate understanding of physical principles and their application. The paper begins with an overview of the fundamental concepts of structural analysis, as well as the basics of Python and SymPy. This is followed by a detailed description of sample problems adapted for SymPy . The results of the exercises and experiments with SymPy was discussed, focussing on the impact of the learning process on the use of the library. Findings reveal that SymPy is a powerful aid in teaching structural analysis, especially when used with Jupyter Notebook.. Students can use it to visualize and solve problems in a fraction of the time required with traditional methods.  It is believed that SymPy’s intuitive approach together with Jupyter Notebook will assist the students gain a deeper understanding of theoretical and analytical concepts, which will be helpful on applying knowledge gained in practice. Overall, the use of SymPy together with Jupyter Notebook in teaching structural analysis appears to be an effective and beneficial approach. Findings demonstrated that SymPy provides students with an intuitive tool to easily explore and understand difficult concepts, allowing them to gain meaningful insights into the theory and practice of structural analysis. Finally, the paper concludes with recommendations on how schools and universities can integrate CAS into their curriculum and cites potential opportunities for further development in the field.