A study on the application of AI agent-based RAG with virtual data in geotechnical engineering

Abstract

The year 2025 has been described as the "first year of AI agents," reflecting growing attention to their capability for autonomous task execution. Despite their potential, AI models face persistent challenges of black-box characteristics, including opaque reasoning processes, limited explainability, and difficulties in validation. This study addresses these issues by generating domain knowledge with GPT-4o, converting it into a vector database, and integrating it with an agentic Retrieval-Augmented Generation (RAG) framework implemented using LangChain and LangGraph. To evaluate the correspondence between knowledge sources and model responses, GPT-5 was employed as an LLM-as-Judge. Through experiments on 15 case studies, the proposed agentic RAG demonstrated improved response quality compared to standalone LLMs and showed potential to mitigate aspects of black-box problems. The findings further suggest applicability to practical fields such as geotechnical engineering, particularly for knowledge transfer, decision support, and on-site problem solving.