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This course provides professional engineers with a comprehensive technical framework for understanding how AI can be integrated into geotechnical engineering workflows while maintaining compliance with established engineering standards and professional responsibilities. Foundational concepts of machine learning, data modeling, and predictive analytics are introduced within the context of geotechnical applications, emphasizing the importance of engineering judgment, model validation, and regulatory compliance. Participants examine how AI tools support site characterization through improved soil classification, parameter estimation, and spatial subsurface modeling using ?
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Learning Objectives:
Upon completion of this course, participants will be able to:
- Explain fundamental artificial intelligence and machine learning concepts relevant to geotechnical engineering applications, including supervised learning, predictive modeling, and data-driven analysis.
- Evaluate how AI tools can enhance geotechnical site characterization through improved soil classification, parameter estimation, spatial modeling, and integration of in situ, laboratory, and geospatial datasets in accordance with applicable engineering standards.
- Analyze the application of AI methods in foundation engineering, including bearing capacity prediction, settlement estimation, deep foundation behavior, and soil-structure interaction modeling while maintaining compliance with governing codes and design frameworks.
- Assess the use of AI in slope stability analysis, earthwork design, groundwater influence evaluation, and landslide susceptibility prediction to support hazard identification and risk-informed engineering decisions.
- Apply AI-based approaches for geotechnical risk prediction and hazard assessment, including liquefaction potential evaluation, infrastructure performance forecasting, sinkhole risk analysis, and probabilistic modeling consistent with reliability-based design principles.
- Examine how AI can support construction monitoring and performance feedback through anomaly detection, predictive modeling of instrumentation data, and integration with the observational method to improve construction safety and decision-making.
- Identify implementation challenges associated with AI adoption, including data limitations, model validation requirements, transparency considerations, ethical obligations, and professional liability risks within the context of engineering practice.
- Develop strategies for integrating AI tools into professional geotechnical engineering workflows, including data management, quality assurance, documentation, training, and communication with stakeholders while maintaining compliance with regulatory and professional standards.
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