Large Language Model for Geotechnical Engineering Applications Using Retrieval Augmented Generation
Bibliographic record
Abstract
The application of Natural Language Processing (NLP) in the field of Civil and geotechnical engineering presents unique challenges with the complexity of the calculations and the necessity for engineering judgment. Large Language Models (LLM) have been successfully presented to be one of the most effective solutions in many specific fields such as arts and literature, finance, and even software development. However, the use of LLM in the field of Civil Engineering and specifically in Geotechnical Engineering has not been widely discussed often compared to the other fields. This paper aims to cover the details with the framework and breakdown of how LLMs can be used to retrieve information from domain-specific data set specifically related to geotechnical engineering. The paper covers topics ranging from settlement analysis, ground improvements, to scripting features with the Rocscience platform. Data sources include research papers, technical documents from geotechnical engineers, and documentation for online help from the relevant geotechnical software. The paper is structured into three main sections: an introduction to LLM models with a focus on pre-trained models and model selection; a discussion of the data retrieval process using domain-specific database using Retrieval Augmented Generation (RAG); and an evaluation of the model’s performance. Performance benchmarks are established with vector embedding to trace context retrieval, and validation is performed using methods such as Recall-Oriented Understudy for Gisting Evaluation (ROGUE), Bidirectional Encoder Representations From Transformer (BERT), and Bilingual Evaluation Understudy (BLEU) score to compare the result with prompt-completion pairs. The study demonstrates that an LLM with sufficient geotechnical domain-specific data source produces superior responses compared to LLM trained on a generic public data set. The paper concludes with a discussion on the comparative results and the future potential of the LLM in geotechnical engineering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".