Become an expert in AI-driven innovation with this 10-module/20-hour online course, designed to equip geotechnical professionals with the practical, hands-on skills needed to apply artificial intelligence (AI) in geotechnical engineering. The curriculum uses industry-standard tools like Python, PyTorch, and SciKit to teach the fundamentals and advanced techniques of machine learning and deep learning for geotechnical applications.
Course Highlights
- Course taught by two University of Texas at Austin award winning educators with extensive related research in both AI and geotechnical engineering: Krishna Kumar, Ph.D. and Ellen Rathje, Ph.D. This ensures the content is current, industry-relevant, and expertly delivered.
- Each module features hands-on coding exercises tied directly to real-world geotechnical challenges.
- Topics are divided into 10, two-hour modules delivered either live or via high-quality recordings, followed by Q&QA sessions.
- Completion of course results in 20 Professional Development Hours (PDHs) and a certificate of completion to validate new expertise in AI-driven geotechnical innovation.
Registration will close at 11:59 p.m. CT, Wednesday, October 29, 2025.
A credit card must be used at the time of payment. Please contact
| Dates | Price | CEUs | Format | |
|---|---|---|---|---|
| Oct. 30 (10 a.m.-5 p.m. CT); Oct. 31 (8 a.m.-12 p.m. CT); Nov. 6(10 a.m.-5 p.m. CT); Nov. 7(8 a.m.-12 p.m. CT) |
$2,000 | 20 Professional Development Hours (PDHs) | Online, Instructor led |
Registration closed. |
10% Discount available to UT System students. Contact:
Key Learning Objectives
- Develop practical AI competence to solve geotechnical challenges.
- Learn cutting-edge AI techniques tailored for geotechnical applications.
Course Takeaways
- Successfully integrate AI techniques into your geotechnical engineering practice.
- Develop and deploy machine learning models to predict soil behavior, assess risk, and optimize engineering operations.
- Interpret AI model outputs using explainable AI techniques to improve decision-making.
- Stay ahead of emerging trends in AI applications within the geotechnical field.
Who Should Take This Course
- Geotechnical Engineers & Practitioners seeking to integrate AI into their work.
- Technical Managers looking to leverage AI for project optimization.
- Industry Innovators interested in adopting cutting-edge methodologies.
- Students interested in AI and Geotechnical Engineering
Course Details
This 10-module online course is designed to provide geotechnical professionals with the practical skills to apply artificial intelligence (AI) in their work. The curriculum uses industry-standard tools like Python, PyTorch, and SciKit-Learn to teach the fundamentals and advanced techniques of machine learning and deep learning for geotechnical applications.
Course Topics:
- Introduction to Machine Learning for Geotechnical Engineering
- Data Preprocessing, Feature Engineering, & Basic Models
- Explainable AI (XAI) with Random Forests for Landslide Risk
- Clustering for Soil Site Profiling & Layering
- Deep Neural Networks (DNNs) & Activation Functions
- DNNs for Tunnel Boring Machine (TBM) Performance
- Bayesian Methods for Geotechnical Uncertainty
- CNNs for Full Waveform Inversion & Image-Based Analysis
- LLMs & RAG for Geotechnical Knowledge Management
- Future Trends & Challenges in AI for Geotechnics
Prerequisites:
Basic Python [ability to write simple functions and classes] to follow along coding example and undergrad geotechnical engineering knowledge.
Instructors
Krishna Kumar, Ph.D.
Dr. Krishna Kumar is the J. Neils Thompson Centennial Teaching Fellow and Assistant Professor in the Fariborz Maseeh Department of Civil, Architectural, and Environmental Engineering at The University of Texas Cockrell School of Engineering.
He is also core faculty at the Oden Institute of Computational Sciences and Engineering at The University of Texas at Austin. Dr. Kumar’s research is at the intersection of AI/ML, geotechnical engineering and robotics. He directs a $7M NSF-funded national ecosystem for AI integration in civil engineering and received an NSF CAREER Award in 2024. His research involves developing differentiable simulations, graph neural networks and numerical methods for understanding natural hazards.
As an educator, Dr. Kumar received the Dean's Award for Outstanding Teaching at UT Austin, teaches at the university level and runs coding clubs at Austin Public Libraries, teaching AI/ML robotics to children ages 7-12. Dr. Kumar also serves as an Associate Editor in International Journal of Rock Mechanics and Geotechnical Engineering and ASCE Journal of Computing in Civil Engineering. Learn more.
Ellen Rathje, Ph.D.
Dr. Ellen Rathje is the Janet S. Cockrell Centennial Chair in Engineering in the Fariborz Maseeh Department of Civil, Architectural, and Environmental Engineering and a Senior Research Fellow at the Bureau of Economic Geology at The University of Texas at Austin.
Her research focuses on geotechnical earthquake engineering and natural hazards, using computational methods, AI/ML, and field observations to understand the impacts of natural hazards on the built environment. Dr. Rathje is a founding member and previous Co-Chair of the Geotechnical Extreme Events Reconnaissance (GEER) Association, and she is currently the Principal Investigator for the DesignSafe cyberinfrastructure that supports research in natural hazards engineering.
She has been honored with the 2022 Peck Lecture Award from the Geo-Institute of the American Society of Civil Engineers (ASCE), the 2018 William B. Joyner Lecture Award from the Seismological Society of America and the Earthquake Engineering Research Institute and the 2010 Huber Research Prize from the ASCE. She is a Fellow of the ASCE and was elected to the prestigious National Academy of Engineering in 2025. Learn more.
