Future-proofing geotechnics workflows: accelerating problem-solving with large language models

Stephen Wu

doi:10.1080/17499518.2024.2381026

Future-proofing geotechnics workflows: accelerating problem-solving with large language models

2024-07-25

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DOI：10.1080/17499518.2024.2381026

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The integration of Large Language Models (LLMs), such as ChatGPT, into the workflows of geotechnical engineering has a high potential to transform how the discipline approaches problem-solving and decision-making. This paper investigates the practical uses of LLMs in addressing geotechnical challenges based on opinions from a diverse group, including students, researchers, and professionals from academia, industry, and government sectors gathered from a workshop dedicated to this study. After introducing the key concepts of LLMs, we present preliminary LLM solutions for four distinct practical geotechnical problems as illustrative examples. In addition to the basic text generation ability, each problem is designed to cover different extended functionalities of LLMs that cannot be achieved by conventional machine learning tools, including multimodal modelling under a unified framework, programming ability, knowledge extraction, and text embedding. We also address the potentials and challenges in implementing LLMs, particularly in achieving high precision and accuracy in specialised tasks, and underscore the need for expert oversight. The findings demonstrate the effectiveness of LLMs in enhancing efficiency, data processing, and decision-making in geotechnical engineering, suggesting a paradigm shift towards more integrated, data-driven approaches in this field.

Keywords:

Large language models

ChatGPT

multimodal modelling

data-driven geotechnical engineering

Future-proofing geotechnics workflows: accelerating problem-solving with large language models

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