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Sponsored by ASCE's Geo-Institute's Technical Committees
INSTRUCTORS:
Chukwuebuka Nweke, Ph.D., EIT, M.ASCE
Kaleigh Yost, Ph.D., EIT. M.ASCE
Katerina Ziotopoulou, Ph.D., P.E., M.ASCE
Purpose and Background
With advances in computational power and availability of many open-source software, new numerical simulations techniques are emerging that have significant potential for tackling very challenging problems in seismic analysis and design of geotechnical systems. This session offers three presentations on the use of an emerging numerical simulation technique and the application of Artificial Intelligence and machine learning in geotechnical earthquake engineering.?
Benefits and Learning Outcomes
Upon completion of these sessions, you will be able to:
- Explain what the new modeling techniques can do in analyzing geotechnical engineering problems.
- Identify the potential applications of artificial intelligence and machine learning in geotechnical earthquake engineering.
- Discuss the advantages and shortcomings of the current modeling techniques used in practice.
- Describe what the emerging modeling techniques can do in solving some important practical problems.
Assessment of Learning Outcomes
Achievement of the learning objectives will be assessed through a short post-test.
Who Should Attend?
- Geotechnical Engineers
- Engineering Geologists
- Researchers
- Graduate Students
- Consultants
- Specialty Contractors
Outline
- Introduction of Topics and Session Sponsors
- Data Science in Geotechnical Earthquake Engineering: Seismic Site Response of Sedimentary Basins – Chukwuebuka Nweke
- Numerical Modeling to Understand Limitations of CPT-Based Liquefaction Hazard Assessment – Kaleigh Yost
- Approaches to Numerical Modelign and Data-Driven Methods in Geotechnical Earthquake Engineering Practice – Katerina Ziotopoulou
How to Earn your CEUs/PDHs and Receive Your Certificate of Completion
This course is worth 0.2 CEUs/2 PDHs. To receive your certificate of completion, you will need to complete a short post-test online and receive a passing score of 70% or higher.
How do I convert CEUs to PDHs?
1.0 CEU = 10 PDHs [Example: 0.1 CEU = 1 PDH]