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

Stephen Wu; Yu Otake; Daijiro Mizutani; Chang Liu; Kotaro Asano; Nana Sato; Hidetoshi Baba; Yusuke Fukunaga; Yosuke Higo; Akiyoshi Kamura; Shinnosuke Kodama; Masataka Metoki; Tomoka Nakamura; Yuto Nakazato; Taiga Saito; Akihiro Shioi; Masahiro Takenobu; Keigo Tsukioka; Ryo Yoshikawa

arXiv · 2312.12411

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

Stephen Wu·Yu Otake·Daijiro Mizutani·Chang Liu·Kotaro Asano·Nana Sato·Hidetoshi Baba·Yusuke Fukunaga·Yosuke Higo·Akiyoshi Kamura·Shinnosuke Kodama·Masataka Metoki·Tomoka Nakamura·Yuto Nakazato·Taiga Saito·Akihiro Shioi·Masahiro Takenobu·Keigo Tsukioka·Ryo Yoshikawa

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Abstract

The integration of Large Language Models (LLMs) like ChatGPT into the workflows of geotechnical engineering has a high potential to transform how the discipline approaches problem-solving and decision-making. This paper delves into the innovative application of LLMs in geotechnical engineering, as explored in a hands-on workshop held in Tokyo, Japan. The event brought together a diverse group of 20 participants, including students, researchers, and professionals from academia, industry, and government sectors, to investigate practical uses of LLMs in addressing specific geotechnical challenges. The workshop facilitated the creation of solutions for four different practical geotechnical problems as illustrative examples, culminating in the development of an academic paper. The paper discusses the potential of LLMs to transform geotechnical engineering practices, highlighting their proficiency in handling a range of tasks from basic data analysis to complex, multimodal problem-solving. It also addresses the challenges in implementing LLMs, particularly in achieving high precision and accuracy in specialized tasks, and underscores the need for expert oversight. The findings demonstrate LLMs' effectiveness in enhancing efficiency, data processing, and decision-making in geotechnical engineering, suggesting a paradigm shift towards more integrated, data-driven approaches in this field. This study not only showcases the potential of LLMs in a specific engineering domain, but also sets a precedent for their broader application in interdisciplinary research and practice, where the synergy of human expertise and artificial intelligence redefines the boundaries of problem-solving.

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Stephen Wu, Yu Otake, Daijiro Mizutani, Chang Liu, Kotaro Asano, Nana Sato, Hidetoshi Baba, Yusuke Fukunaga, Yosuke Higo, Akiyoshi Kamura, Shinnosuke Kodama, Masataka Metoki, Tomoka Nakamura, Yuto Nakazato, Taiga Saito, Akihiro Shioi, Masahiro Takenobu, Keigo Tsukioka, Ryo Yoshikawa. 2023-12-14. Future-proofing geotechnics workflows: accelerating problem-solving with large language models. https://arxiv.org/abs/2312.12411

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