GeoLLM: A Technical Exploration of Automatically Interpreting Geotechnical Engineering Reports Using Large Language Models

The GeoLLM project demonstrates how to apply Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) technologies to the intelligent parsing of geotechnical engineering reports. Addressing the pain points of low efficiency and high error rates in traditional manual processing, it provides a practical automated data processing solution, offering a reference for the digital transformation of this field.

Geotechnical engineering reports carry key information such as underground soil layer structure and soil mechanical properties. However, due to inconsistent formats (PDF/scanned documents) and dense professional terminology, manual processing is inefficient and error-prone. GeoLLM is an intelligent parsing tool customized specifically for this field, integrating engineering semantic understanding, professional constraint verification, and structured output capabilities to ensure that extracted data can be directly used for subsequent design verification and analysis.

GeoLLM adopts a modular layered architecture:

1. Document Processing Layer: PDF text extraction, OCR recognition, content chunking, converting unstructured documents into processable fragments;
2. Vectorization and Retrieval Layer: Using domain-specific embedding models to store text fragments in a vector database and retrieve relevant context;
3. Generation Layer: Based on the RAG architecture, generating structured output by combining context and prompt templates, which is then presented in JSON/Excel format after engineering rule verification.

GeoLLM's domain adaptation optimizations include:

1. Prompt Engineering: Designing professional extraction templates to guide the model in understanding geotechnical terms (e.g., silty clay, moderately weathered sandstone) and their associations with engineering parameters;
2. Structured Output: Using Pydantic to define JSON Schema, ensuring the output conforms to the expected structure;
3. Quality Control: Built-in numerical range checks, unit consistency verification, and cross-document cross-checking to reduce error risks.

Application scenarios include:

1. Survey Report Collation: Automatically extracting borehole locations, soil layer stratification, mechanical indicators, etc., reducing manual entry time from hours to minutes;
2. Design Verification: Quickly comparing survey data from multiple sources to identify contradictions;
3. Synthetic Data Generation: Generating simulated reports for model training and teaching cases.

Current Limitations: Challenges remain in scanned image quality, handwritten annotation recognition, and complex table parsing; deviations may occur when handling rare geological conditions or special engineering agreements. Future Directions: Short-term goals include improving the Streamlit interface, expanding document types, and establishing a testing system; medium-to-long-term goals involve building a 'Geotechnical Engineering AI Assistant' to enable preliminary engineering judgments and recommendations.

GeoLLM provides a methodology for AI applications in the engineering field: it is necessary to deeply integrate domain knowledge instead of applying general tools, and to understand engineering processes, norms, and pain points. For enterprises and institutions undergoing digital transformation, it offers a practical sample of building data pipelines, intelligent engines, and application interfaces starting from business pain points.