构建首个多层级施工安全数据集，助力智能风险预警
Building Safer Sites: A Large-Scale Multi-Level Dataset for Construction Safety Research
- 整合官方记录的事故、检查与违规数据，含结构化属性与原始描述
- 发现投诉驱动检查可降低17.3%后续事故概率
- 适合研究施工安全、机器学习应用的学者与工程管理人员
施工安全研究是土木工程中的关键领域，旨在通过分析现场状况与人为因素来降低风险、预防伤害。然而，现有施工安全数据集规模有限且多样性不足，制约了深入分析。为此，本文提出施工安全数据集（CSDataset），一个结构清晰、涵盖事故、检查与违规记录的综合性多层级数据集，数据来源为美国职业安全与健康管理局（OSHA）。该数据集首次融合结构化属性与非结构化文本描述，支持机器学习与大语言模型等多种方法研究。我们还基于该数据集进行了初步基准测试与跨层级分析，例如发现投诉驱动的检查可使后续事故发生概率降低17.3%。相关数据与代码已开源至 https://github.com/zhenhuiou/Construction-Safety-Dataset-CSDataset。
原文摘要 · Abstract (English)
Construction safety research is a critical field in civil engineering, aiming to mitigate risks and prevent injuries through the analysis of site conditions and human factors. However, the limited volume and lack of diversity in existing construction safety datasets pose significant challenges to conducting in-depth analyses. To address this research gap, this paper introduces the Construction Safety Dataset (CSDataset), a well-organized comprehensive multi-level dataset that encompasses incidents, inspections, and violations recorded sourced from the Occupational Safety and Health Administration (OSHA). This dataset uniquely integrates structured attributes with unstructured narratives, facilitating a wide range of approaches driven by machine learning and large language models. We also conduct a preliminary approach benchmarking and various cross-level analyses using our dataset, offering insights to inform and enhance future efforts in construction safety. For example, we found that complaint-driven inspections were associated with a 17.3% reduction in the likelihood of subsequent incidents. Our dataset and code are released at https://github.com/zhenhuiou/Construction-Safety-Dataset-CSDataset.
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