[Latest] Multilingual Strategy to Eliminate Near-Misses: High-Accuracy Translation of Safety and Health Management Plans via LLMs

As the ratio of foreign workers at construction sites rapidly increases, the most serious challenge is the risk of occupational accidents due to "language barriers" [1]. Conventional machine translation has struggled to accurately convey site-specific terminology and context-dependent safety instructions found in Japan-specific documents such as "Construction Procedure Manuals" and "Health and Safety Management Plans." In this article, we will explain a next-generation multilingual strategy that combines LLMs (Large Language Models) and RAG (Retrieval-Augmented Generation) to fundamentally eliminate "near-miss" incidents caused by mistranslation.

A high-tech digital dashboard displaying a Japanese construction site safety plan being translated into multiple languages using AI. The screen shows data visualizations of risk assessment and real-time translation accuracy metrics in a clean, professional Japanese office environment.

1. Limitations of Conventional Translation and the Barrier of "Field-Specific Terminology"

On construction sites, specialized terms not found in standard dictionaries—such as "tateire-naoshi," "aiban," and "engiri"—frequently appear. Running these through standard translation engines often results in literal translations that ignore context, leading workers to follow incorrect construction procedures. In particular, misinterpreting instructions in safety and health management plans creates "information discrepancies" that can lead directly to serious accidents.

A Japanese data scientist analyzing a complex network graph of Japanese construction terminology on a large monitor. The interface shows how specific kanji compounds are mapped to technical English and Vietnamese equivalents to ensure zero ambiguity in safety manuals.

2. Enhancing the Accuracy of Safety Management Plans Leveraging RAG

In the latest AI strategies, rather than simply having an LLM translate, we incorporate **RAG (Retrieval-Augmented Generation)** [2]. By creating a database of a company's past construction records, glossaries, and safety manuals and having the AI refer to them during translation, we ensure the selection of appropriate terminology tailored to the field. As a result, translation accuracy has improved dramatically compared to conventional machine translation.

Q. Is multilingual support (Vietnamese, Indonesian, etc.) available?
A. Yes. Major LLMs support more than 100 languages. By utilizing RAG, high translation quality can be maintained even for Southeast Asian languages, which are in high demand at construction sites.
Q. How much preparation time is required before implementation?
A. It depends on the current state of your existing manual digitization, but a prototype can be operational in as little as two months. We recommend starting small with a specific work category first.

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Summary

Multilingualization of construction sites is not merely a welfare benefit, but a "safety investment" to avoid management risks. By utilizing high-precision translation combining LLM and RAG to ensure the true intent of construction manuals is understood by all workers, near-miss incidents can be dramatically reduced. Overcoming the "language barrier" through technology will be the most critical challenge in future construction DX.

Published: May 22, 2024 / By: Osamu Yasuda

WRITTEN BY
Osamu Yasuda

Osamu Yasuda

Senior Managing Director & COO

Meets Consulting Inc.

References

  • [1] Ministry of Health, Labour and Welfare, "Safety and Health Management of Foreign Workers in the Construction Industry"
  • [2] OpenAI「Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks」
Disclaimer: This article is for informational purposes only and is not intended as a substitute for professional advice. It does not guarantee specific results.