Domain adaptation of large language models for geotechnical applications

TL;DR

This paper reviews how large language models can be adapted for geotechnical engineering, highlighting strategies, applications, benefits, and challenges to guide future research and practical implementation.

Contribution

It provides the first systematic review of LLM adaptation strategies specifically for geotechnical applications, analyzing their benefits and limitations.

Findings

Domain-adapted LLMs improve reasoning accuracy and automation.

Adaptation strategies face challenges like data scarcity and validation.

The review guides future development of geotechnically literate LLMs.

Abstract

The rapid advancement of large language models (LLMs) is transforming opportunities in geotechnical engineering, where workflows rely on complex, text-rich data. While general-purpose LLMs demonstrate strong reasoning capabilities, their effectiveness in geotechnical applications is constrained by limited exposure to specialized terminology and domain logic. Thus, domain adaptation, tailoring general LLMs for geotechnical use, has become essential. This paper presents the first systematic review of LLM adaptation and application in geotechnical contexts. It critically examines four key adaptation strategies, including prompt engineering, retrieval augmented generation, domain-adaptive pretraining, and fine-tuning, and evaluates their comparative benefits, limitations, and implementation trends. This review synthesizes current applications spanning geological interpretation, subsurface characterization, design analysis, numerical modeling, risk assessment, and geotechnical education. Findings show that domain-adapted LLMs substantially improve reasoning accuracy, automation, and interpretability, yet remain limited by data scarcity, validation challenges, and explainability concerns. Future research directions are also suggested. This review establishes a critical foundation for developing geotechnically literate LLMs and guides researchers and practitioners in advancing the digital transformation of geotechnical engineering.