Artificial Intelligence Enabled Geo-Engineering
The rapid digitalization of civil infrastructure and the exponential growth of geological data are reshaping the landscape of geotechnical and geological engineering. As infrastructure projects become increasingly complex and enter more challenging geological environments, traditional empirical and analytical methods often struggle to handle the high dimensionality, non-linearity, and inherent uncertainty of geological conditions. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics offers a transformative solution, shifting the paradigm from experience-based to data-centric engineering.
This special session aims to gather researchers, engineers, and data scientists to discuss the state-of-the-art applications of AI in geo-engineering. The goal is to bridge the gap between advanced computational algorithms and practical geotechnical challenges. We seek to explore how artificial intelligence can automate site characterization, enhance constitutive modeling, optimize design and construction processes, and improve the resilience of geo-structures against natural hazards. Special attention will be given to interpretable AI and physics-informed strategies that ensure reliability and physical consistency in geo-engineering applications.
We invite abstract submissions on topics including, but not limited to:
(1) Intelligent Site Characterization & Data Analysis
- Computer vision and deep learning for rock mass classification and soil stratigraphy identification.
- Subsurface data interpolation and 3D geological modeling using geo-statistics and machine learning.
- Handling sparse, noisy, and heterogeneous geotechnical data: Data augmentation and generation strategies.
- Automated processing of geo-data and laboratory test results.
(2) Advanced Modeling & Predictive Analytics
- Physics-Informed Machine Learning (i.e., PINNs) for solving complex boundary value problems in geomechanics.
- Data-driven constitutive modeling and parameter identification for soils and rocks.
- Surrogate modeling for real-time prediction of slope stability, tunnel convergence, and foundation settlement.
- Generative AI and Large Language Models (LLMs) for geotechnical report analysis and decision support.
(3) Smart Construction & Risk Management
- AI-driven optimization for TBM tunneling, drilling, and excavation parameters.
- Real-time monitoring and inverse analysis: Updating design parameters based on field performance data.
- Intelligent risk assessment and early warning systems for landslides, debris flows, and urban geohazards.
- Digital Twin integration: Linking AI algorithms with BIM and sensor networks for lifecycle management.