Building Safer Sites: A Large-Scale Multi-Level Dataset for Construction Safety Research

TL;DR

This paper introduces a comprehensive multi-level construction safety dataset (CSDataset) sourced from OSHA, enabling advanced analysis and machine learning applications to improve safety outcomes in civil engineering.

Contribution

The paper presents a novel, large-scale, multi-level construction safety dataset integrating structured and unstructured data, facilitating diverse research and machine learning approaches.

Findings

Complaint-driven inspections linked to 17.3% fewer incidents.

Benchmarking of preliminary approaches demonstrates dataset utility.

Cross-level analysis offers new safety insights.

Abstract

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.