Programming Geotechnical Reliability Algorithms using Generative AI

TL;DR

This paper investigates how Large Language Models like ChatGPT can automate and accelerate the generation of reliability algorithms in geotechnical engineering, showing promising results and discussing limitations.

Contribution

It demonstrates the feasibility of using LLMs to generate MATLAB codes for classical reliability algorithms in geotechnical engineering, a novel application of AI in this field.

Findings

LLMs can generate MATLAB codes for reliability algorithms.

Generated codes show comparable results to benchmark methods.

Challenges and limitations of LLMs are identified.

Abstract

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.