Future-proofing geotechnics workflows: accelerating problem-solving with
large language models
- URL: http://arxiv.org/abs/2312.12411v1
- Date: Thu, 14 Dec 2023 05:17:27 GMT
- Title: Future-proofing geotechnics workflows: accelerating problem-solving with
large language models
- Authors: 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, and Ryo Yoshikawa
- Abstract summary: This paper delves into the innovative application of Large Language Models in geotechnical engineering, as explored in a hands-on workshop held in Tokyo, Japan.
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 problem-solving.
- Score: 2.8414492326907577
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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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