About our Sessions

AI-assisted Geotechnical Engineering

This talk will explore AI approaches such as Large Language Models (GPT-like) to design foundations, differentiable simulations for inverse analysis and monitoring of landslides and design of protective barriers.

The widespread adoption of large language models (LLMs), such as OpenAI’s ChatGPT, could revolutionize various industries, including geotechnical engineering. However, GPT models can sometimes generate plausible-sounding but false outputs, leading to hallucinations. We discuss the importance of prompt engineering in mitigating these risks and harnessing the full potential of GPT for civil engineering applications. We explore the challenges and pitfalls associated with LLMs and highlight the role of context in ensuring accurate and valuable responses. By integrating GPT into foundation design workflows, professionals can streamline their work and develop sustainable and resilient infrastructure systems for the future.

Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on the target runout profile. Traditional high-fidelity simulators for these inverse problems are computationally demanding, restricting the number of simulations possible. Additionally, their non-differentiable nature makes gradient-based optimization methods, known for their efficiency in high-dimensional problems, inapplicable. While machine learning-based surrogate models offer computational efficiency and differentiability, they often struggle to generalize beyond their training data due to their reliance on low-dimensional input-output mappings that fail to capture the complete physics of granular flows. We propose a novel differentiable graph neural network simulator (GNS) by combining reverse mode automatic differentiation of graph neural networks with gradient-based optimization for solving inverse problems. GNS learns the dynamics of granular flow by representing the system as a graph and predicts the evolution of the graph at the next time step, given the current state. The differentiable GNS shows optimization capabilities beyond the training data. We demonstrate the effectiveness of our method for inverse estimation across single and multi-parameter optimization problems, including evaluating material properties and boundary conditions for a target runout distance and designing baffle locations to limit a landslide runout. The talk will also explore explainable AI for looking at decision making under AI predictions.



Krishna Kumar Ph.D.
Associate Professor
The University of Texas at Austin


Call: 512-472-8905

Email: office@texasce.org

For more info, visit Contact Us