GeoLLM: A specialized large language model framework for intelligent geotechnical design
Year: 2024
Type: article
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.
Source: Computers and Geotechnics
Institution: Hong Kong Polytechnic University
Language: English
Subfield: General Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,580
Awards: Deep Learning-based Multiscale Approach for Granular Materials from Particle Recognition to Mechanical Modelling, Physics-informed multi-fidelity neural network approach for intelligent rectification of shield machine attitude in layered soils, Machine Learning-Aided Multiscale Approach for Recycled Solid Waste Mixtures Composed of Easily Crushable Rigid Aggregates and Highly Deformable Soft Aggregates, Investigation of grain breakage and seepage erosion in carbonate sand based on a multi-scale hydro-mechanical approach, G8117808562, G8722319177
OpenAlex ID: W4403734358