This document discusses machine learning applications in structural design. It provides examples of using machine learning for stock market prediction, document classification, and spam filtering. It also outlines different types of machine learning models including supervised learning techniques like linear regression, classification, and unsupervised learning clustering. The document proposes using machine learning to assist with structural connection design by automatically adding connections to 3D models to boost productivity. It presents a case study of applying decision tree machine learning to member data and coordinates to output connection drawings and details the process.

1. Machine Learning applied
in Structure Design
Internal Presentation
Khoa Nguyen
Sep 2023

2. Contact

3. What is Machine Learning

4. ML in Action
Stock Market Prediction

5. ML in Action
Document classification

6. ML in Action
Spam filtering

7. Types of ML

8. Types of ML
Supervised Learning – Linear regression
Prediction

9. Supervised Learning – Linear regression
Train / Val / Test set

10. Supervised Learning – Linear regression
Train / Val / Test set

11. Types of ML
Supervised Learning – Classification

12. Types of ML
Unsupervised Learning – Clustering

13. Types of ML
Reinforcement Learning

14. Step by step

15. Application
Currently, machine learning brings a lot of
benefits to many different fields and professions.
Not out of that trend, the application of the digital
field and the benefits of machine learning to the
structural design field helps to speed up the work
and the accuracy of the work without having to
manipulate it manually.

16. Problem
Hong Kong International Airport
In the steel connection
design field, let’s imagine a
project has about 5000
connections:
• ML can help us add the
desired connections in
bulk with a large
number to the 3D model
• Saving working time
• Boosting productivity

17. Objective
Member Data & Point
Coordinates
Decision Tree –
Machine Learning
3D model

18. Input
Node Coordinate
Analysis Model
Member Data

19. Output
Output Data
Drawings

20. Process
Import modules
and read files
Define functions
and process data
Use the created
data to apply to
another dataset
by Machine
Learning
Format result

21. Model – Decision Tree

22. Comparison
Classification based on logit in grasshopper and based on ML application

23. Comparison
Classification based on logit in grasshopper and based on ML application

24. Other applications
Geotechnical Parameters

25. Other applications
Pile layout