RAG-Enhanced Safety Information Retrieval for Construction: Integration of Large Language Models with Domain-Specific Information
Bibliographic record
Abstract
In the construction industry, critical safety information is often scattered across numerous documents, standards, and regulations, making it challenging for practitioners to access and comprehend safety knowledge in their daily operations efficiently.To address this challenge, we propose an intelligent and reliable questionanswering system for information retrieval and response generation on the construction health, safety, and environment documents via retrieval-augmented generation.Specifically, our system combines a finetuned LLaMA-3-8B base model with a vector database constructed using embedding models, enabling accurate information retrieval and enhancing the generated responses' reliability.Initial validation using cosine similarity analysis demonstrates promising results, with our system achieving a cosine similarity score of 0.936, outperforming the LLAMA3-8B base model's score of 0.884 in processing construction safety documentation.The preliminary findings show that: 1) our RAG-enhanced system provides safety information access, and 2) our specialized preprocessing techniques effectively synthesize and retrieve safety information, reducing fragmentation and access time.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".