Programming Geotechnical Reliability Algorithms using Generative AI
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913910253158400 |
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| author | Sharma, Atma Zhang, Jie Lu, Meng Wu, Shuangyi Li, Baoxiang |
| author_facet | Sharma, Atma Zhang, Jie Lu, Meng Wu, Shuangyi Li, Baoxiang |
| contents | Programming reliability algorithms is crucial for risk assessment in geotechnical engineering. This study explores the possibility of automating and accelerating this task using Generative AI based on Large Language Models (LLMs). Specifically, the most popular LLM, i.e., ChatGPT, is used to test the ability to generate MATLAB codes for four classical reliability algorithms. The four specific examples considered in this study are: (1) First Order Reliability Method (FORM); (2) Subset simulation; (3) Random field simulation; and (4) Bayesian update using Gibbs sampling. The results obtained using the generated codes are compared with benchmark methods. It is found that the use of LLMs can be promising for generating reliability codes. Failure, limitations, and challenges of adopting LLMs are also discussed. Overall, this study demonstrates that existing LLMs can be leveraged powerfully and can contribute toward accelerating the adoption of reliability techniques in routine geotechnical engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Programming Geotechnical Reliability Algorithms using Generative AI Sharma, Atma Zhang, Jie Lu, Meng Wu, Shuangyi Li, Baoxiang Applications Programming reliability algorithms is crucial for risk assessment in geotechnical engineering. This study explores the possibility of automating and accelerating this task using Generative AI based on Large Language Models (LLMs). Specifically, the most popular LLM, i.e., ChatGPT, is used to test the ability to generate MATLAB codes for four classical reliability algorithms. The four specific examples considered in this study are: (1) First Order Reliability Method (FORM); (2) Subset simulation; (3) Random field simulation; and (4) Bayesian update using Gibbs sampling. The results obtained using the generated codes are compared with benchmark methods. It is found that the use of LLMs can be promising for generating reliability codes. Failure, limitations, and challenges of adopting LLMs are also discussed. Overall, this study demonstrates that existing LLMs can be leveraged powerfully and can contribute toward accelerating the adoption of reliability techniques in routine geotechnical engineering. |
| title | Programming Geotechnical Reliability Algorithms using Generative AI |
| topic | Applications |
| url | https://arxiv.org/abs/2506.19536 |