- Demonstrates how Python automation integrates into modeling, analysis, and reporting workflows in MIDAS CIVIL NX.
- Shows automated processes such as extracting result images and tables, and generating Excel reports.
- Explains practical use cases like updating alignment through Python code and rerunning analyses efficiently.
- Connects automation concepts with real project workflows to reduce manual errors and repetitive work.
Your submission has been
successfully received
If you meet the certification criteria,
Your Certificate of Completion will be sent to your email.
If you need urgent support, contact us via here.
We truly appreciate your participation!
Available after Registration
Full access is available upon registration.
Complete the form beside to continue and use the service.
Description
Reduce repetitive engineering work without giving up engineering judgment.
This on-demand session focuses on practical Python automation for structural engineering workflows, helping engineers reduce repetitive tasks without changing core engineering judgment.
How It Works
-
STEP 1 – Register
Fill out the form and submit it -
STEP 2 – Watch
Watch the on-demand webinar anytime. Expand practical insights, real projects, and immediate application.
Trial & Certificate Schedule
-
1-week trial license: January 28 – February 4, 2026
-
Assignment submission deadline: February 4, 2026 (KST)
-
Certificate delivery to qualified participants : February 20, 2026
On-Demand Session Overview
Understanding Python Automation in Structural Engineering

Learn how Python automation boosts structural engineering efficiency and reduces manual errors.

Discover practical Python integration in MIDAS CIVIL NX through expert-led real-world cases.
Automate, Optimize, Accelerate:
Structural Engineering with Python
- Overview of automation in structural engineering
- How Python fits into modeling, analysis, and reporting workflows
- Mini-assignment to reinforce key concepts through simple hands-on practice
This session is for you if you are…
Repeating the same modeling and analysis steps across projects
Constantly updating inputs and rerunning analyses as conditions change
Spending too much time on tasks that are not difficult but highly repetitive
Speaker