Course overview and expectations

• Interpret built-environment datasets and communicate findings visually
• Prepare and clean construction-related data for analysis
• Apply introductory machine learning and deep learning methods to domain-specific problems
• Understand how sensing, computer vision, and digital twins connect to construction practice

• Study generative AI workflows for design communication and scenario exploration
• Use Google Colab to run notebook-based learning activities
• Interpret AI tools critically in relation to safety, operations, and project delivery
• Develop and present project-based work grounded in real built-environment questions

Module 1

Data Visualization and Visual Analytics for the Built Environment

Charts and visual analysis for construction datasets.

Module 2

Data Sensing and Site Monitoring Systems

Sensor-based workflows for environmental conditions and field performance.

Module 3

Construction Data Preprocessing and Quality Control

Cleaning, structuring, and validating project data before analysis and modeling.

Module 4

Machine Learning for Construction

Machine learning workflows for concrete performance and permit datasets.

Module 5

Deep Learning for Construction

Concepts, methods, and examples in deep learning for construction.

Module 6

Digital Twins and Robotics for Construction

Digital twin workflows, robotics applications, and interactive simulation for construction.

Module 7

Generative Models for Design Communication

Diffusion-based image generation for concept development and design communication.

Module 8

Open Exploration with Instructor Support

Course project development with instructor feedback, open-ended exploration, and final reporting.