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GeoLLM: A specialized large language model framework for intelligent geotechnical design.

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Title: GeoLLM: A specialized large language model framework for intelligent geotechnical design.
Authors: Xu, Hao-Ruo1 (AUTHOR) haoruo.xu@connect.polyu.hk, Zhang, Ning1,2 (AUTHOR) ning-cee.zhang@polyu.edu.hk, Yin, Zhen-Yu2 (AUTHOR) zhenyu.yin@polyu.edu.hk, Guy Atangana Njock, Pierre1 (AUTHOR) pierre-guy.atangananjock@polyu.edu.hk
Source: Computers & Geotechnics. Jan2025:Part A, Vol. 177, pN.PAG-N.PAG. 1p.
Subjects: Language models, Information professionals, Engineering models, Industrial research
Abstract: Large language models (LLMs) have achieved remarkable success in various industrial and research fields, enhancing work efficiency by assisting machines in comprehending human language. In geotechnical design where extensive repetitive cross-checking of design codes consumes considerable time and labour, the utilization of LLMs to enhance design procedures has not been explored before. The challenge is to ensure that LLMs accurately comprehend professional geotechnical information from text and execute mathematical calculations correctly. This study makes the first attempt at developing a specialized LLM framework, GeoLLM, integrated with an innovative prompt engineering strategy to extract professional information from text and enable accurate mathematical calculations. GeoLLM is applied to the design of single piles involving bearing capacity and settlement calculations. The results reveal that GeoLLM exhibits excellent performance in single pile cases. Additionally, compared with LLMs of varying architectures and sizes, commercial LLMs with over 100 billion parameters presented outstanding comprehensive capacities, while those with 1.8 ∼ 72 billion parameters degraded relatively. These findings indicate the promising capacity of GeoLLM to address professional tasks in geotechnical design. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Geotechnics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 180994818
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: GeoLLM: A specialized large language model framework for intelligent geotechnical design.
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  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Hao-Ruo%22">Xu, Hao-Ruo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> haoruo.xu@connect.polyu.hk</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Ning%22">Zhang, Ning</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ning-cee.zhang@polyu.edu.hk</i><br /><searchLink fieldCode="AR" term="%22Yin%2C+Zhen-Yu%22">Yin, Zhen-Yu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhenyu.yin@polyu.edu.hk</i><br /><searchLink fieldCode="AR" term="%22Guy+Atangana+Njock%2C+Pierre%22">Guy Atangana Njock, Pierre</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pierre-guy.atangananjock@polyu.edu.hk</i>
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  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Geotechnics%22">Computers & Geotechnics</searchLink>. Jan2025:Part A, Vol. 177, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Information+professionals%22">Information professionals</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+models%22">Engineering models</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+research%22">Industrial research</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Large language models (LLMs) have achieved remarkable success in various industrial and research fields, enhancing work efficiency by assisting machines in comprehending human language. In geotechnical design where extensive repetitive cross-checking of design codes consumes considerable time and labour, the utilization of LLMs to enhance design procedures has not been explored before. The challenge is to ensure that LLMs accurately comprehend professional geotechnical information from text and execute mathematical calculations correctly. This study makes the first attempt at developing a specialized LLM framework, GeoLLM, integrated with an innovative prompt engineering strategy to extract professional information from text and enable accurate mathematical calculations. GeoLLM is applied to the design of single piles involving bearing capacity and settlement calculations. The results reveal that GeoLLM exhibits excellent performance in single pile cases. Additionally, compared with LLMs of varying architectures and sizes, commercial LLMs with over 100 billion parameters presented outstanding comprehensive capacities, while those with 1.8 ∼ 72 billion parameters degraded relatively. These findings indicate the promising capacity of GeoLLM to address professional tasks in geotechnical design. [ABSTRACT FROM AUTHOR]
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  Label:
  Group: Ab
  Data: <i>Copyright of Computers & Geotechnics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.compgeo.2024.106849
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      – Code: eng
        Text: English
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      – SubjectFull: Language models
        Type: general
      – SubjectFull: Information professionals
        Type: general
      – SubjectFull: Engineering models
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      – SubjectFull: Industrial research
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      – TitleFull: GeoLLM: A specialized large language model framework for intelligent geotechnical design.
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            – D: 01
              M: 01
              Text: Jan2025:Part A
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              Y: 2025
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