Webinar
Ongoing Registration In Progress Closed
MIDAS GEOTECH
[EXPERT WEBINAR] Digital Geotechnical Engineering:
AI Application to Urban Disaster
    | 10:00 – 11:00 (GMT+7, Thailand Time) / 11:00 - 12:00 (GMT+8, Singapore Time)
Artificial Intelligence(AI), Slope Stability, Urban Disaster Resilience, Digital Geotechnical Egnineering
About the Seminar

Artificial Intelligence (AI) is reshaping how geotechnical engineers understand, predict, and respond to urban disasters. To effectively harness AI, engineers must first master a structured workflow — from defining engineering problems and curating reliable datasets to selecting suitable models and interpreting outputs within meaningful geotechnical contexts.

This webinar introduces a step-by-step application of AI in slope engineering, demonstrating how digital methods can transform traditional analysis into a real-time decision-making process. The session will explore the estimation of effective cohesion (c′) and the direct prediction of the factor of safety (FOS) under rainfall-induced slope failure conditions.


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Key Highlights

-  Learn the complete workflow for integrating AI into geotechnical engineering.

-  Understand the transition from object detection to instance segmentation for slope failure prediction.

-  Discover how AI can deliver pixel-level accuracy and drastically reduce computational time.

-  Explore applications in data-sparse, landslide-prone urban areas.

-  Gain practical insights into digital disaster preparedness and sustainable development.

This webinar is ideal for:

-  Geotechnical engineers and researchers exploring AI integration

-  Civil and infrastructure consultants

-  Academic professionals in geotechnical and data-driven engineering

-  Government and municipal agencies involved in disaster risk management

By the end of this session, Participants will:

1.  Understand how to structure an AI-driven geotechnical workflow. 

2.  Recognize how digital tools can enhance slope stability assessment.

3. Gain confidence in interpreting AI-based predictions in engineering contexts.

4. Be equipped to apply data-driven methods for disaster mitigation and urban resilience.

SPEAKERS
  • Dr. Yongmin Kim Faculty Member, James Watt School of Engineering, University of Glasgow

    He is a faculty member at the James Watt School of Engineering, University of Glasgow. His research and teaching center on urban disasters and sustainable urban development, adopting interdisciplinary approaches that connect geotechnical engineering, digital technologies, and data-driven methodologies.

    He has built a strong international network that spans both academia and the construction industry, fostering broad and trusted collaborations. With a distinguished record in research and education, Dr. Kim combines technical depth with practical perspective, establishing himself as a forward-looking expert in geotechnical engineering.