LM-DSB: A Lightweight Multi-agent System for Automated Daily Safety Bulletin Generation in Construction

Authors: Xianxiang Zhao , Advik Mehta , Falak Sethi , Brian Gue , Qipei Mei , Lingzi Wu

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Abstract: Construction safety management still struggles to produce daily safety bulletins (DSBs), structured daily communications that identify site-specific hazards, convey safety protocols, and set preventive measures for workers. Manual DSB creation takes 2-3 hours daily and yields inconsistent quality, with 25% of crews never conducting pretask planning (Olson et al., 2016), diverting safety personnel from field supervision. This study proposes the Lightweight Multi-Agent DSB System (LM-DSB), which integrates retrieval-augmented generation (RAG) with a locally deployable Large Language Model (LLM) to automate DSB generation on consumer-grade personal computers. LM-DSB coordinates specialized planner, researcher, and writer agents in a hierarchical architecture, enhanced by domain-specific fine-tuning on construction safety documentation and RAG grounding in 37 health, safety, and environment (HSE) documents. A two-phase validation assessed feasibility. Phase 1 comparative evaluation showed the multi-agent RAG system (Llama 3-8B) outperformed all alternative configurations, with strong lexical alignment and coherence (TF-IDF: 0.70, BLEU: 0.39, ROUGE-1: 0.66) and near-complete document generation (length ratio: 1.01 vs. 0.39 for single-agent RAG). Phase 2 expert evaluation (N=40) confirmed practical acceptability (mean scores 3.50-4.20 on a 5-point scale), particularly in regulatory compliance (4.20) and factual accuracy (4.10). The findings show that architectural design enables lightweight models to handle safety-critical documentation on consumer-grade hardware, advancing technology democratization in construction safety management. Limitations include knowledge base scope, language constraints, and lack of field deployment validation; future work should address multimodal integration and workflow adoption.

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.36433 [cs.AI]

(or

arXiv:2610.36433v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36433

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arXiv-issued DOI via DataCite (pending registration)

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From: Xianxiang Zhao [ view email ]

[v1]

Tue, 27 Oct 2026 00:29:20 UTC (1,323 KB)

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