Future-proofing geotechnics workflows: accelerating problem-solving with large language models
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.
Cite
@article{arxiv.2312.12411,
title = {Future-proofing geotechnics workflows: accelerating problem-solving with large language models},
author = {Stephen Wu and Yu Otake and Daijiro Mizutani and Chang Liu and Kotaro Asano and Nana Sato and Hidetoshi Baba and Yusuke Fukunaga and Yosuke Higo and Akiyoshi Kamura and Shinnosuke Kodama and Masataka Metoki and Tomoka Nakamura and Yuto Nakazato and Taiga Saito and Akihiro Shioi and Masahiro Takenobu and Keigo Tsukioka and Ryo Yoshikawa},
journal= {arXiv preprint arXiv:2312.12411},
year = {2023}
}
Comments
Supplementary information will be available upon request