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

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.