Multi-label material and human risk factors recognition model for construction site safety management

IF 3.9 2区 工程技术 Q1 ERGONOMICS Journal of Safety Research Pub Date : 2024-10-09 DOI:10.1016/j.jsr.2024.10.002
Jeongeun Park , Sojeong Seong , Soyeon Park , Minchae Kim , Ha Young Kim
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引用次数: 0

Abstract

Introduction: Construction sites are prone to numerous safety risk factors, but safety managers have difficulty managing these risk factors for practical reasons. Moreover, manually identifying multiple risk factors visually is challenging. Therefore, this study aims to propose a deep learning model–based multi-label risk factor recognition (MRFR) framework that automatically recognizes multiple potential material and human risk factors at construction sites. The research answers the following questions: How can a deep learning model be developed and optimized to recognize and classify multiple material and human risk factors automatically and concurrently at construction sites, and how can the decision-making process of the model be understood and improved for practical application in preemptive safety management? Methods: Data comprising 14,605 instances of eight types of material and human risk factors were collected from construction sites. Multiple risk factors can occur concurrently; thus, an optimal model for multi-label recognition of possible risk factors was developed. Results: The MRFR framework combines material and human risk factors into a single label while achieving satisfactory performance with an F1 score of 0.9981 and a Hamming loss of 0.0008. The causes of mispredictions by MRFR were analyzed by interpreting the decision basis of the model using visualization. Conclusion: This study found that the model must have sufficient capacity to detect multiple risk factors. Performance degradation in MRFR is primarily due to difficulties recognizing visual ambiguities and a tendency to focus on nearby objects when perspective is involved. Practical applications: This study contributes to safety management knowledge by developing a model to recognize multi-label material and human risk factors. Furthermore, the results can be used as guidelines for data collection methods and model improvement in the future. The MRFR framework can be used as an algorithm to recognize risk factors preemptively and automatically at real-world construction sites.
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用于建筑工地安全管理的多标签材料和人为风险因素识别模型
导言:建筑工地容易出现许多安全风险因素,但由于实际原因,安全管理人员很难管理这些风险因素。此外,手动直观地识别多种风险因素也具有挑战性。因此,本研究旨在提出一种基于深度学习模型的多标签风险因素识别(MRFR)框架,该框架可自动识别建筑工地潜在的多种物质和人为风险因素。本研究回答了以下问题:如何开发和优化深度学习模型,以自动并发识别建筑工地上的多种材料和人为风险因素并对其进行分类,以及如何理解和改进该模型的决策过程,以便在先期安全管理中实际应用?方法:从建筑工地收集了 14,605 个数据,包括八种物质和人为风险因素。多种风险因素可能同时出现,因此开发了一种对可能的风险因素进行多标签识别的最佳模型。结果:MRFR 框架将材料和人为风险因素合并为一个标签,同时取得了令人满意的性能,F1 分数为 0.9981,汉明损失为 0.0008。通过可视化解释模型的决策依据,分析了 MRFR 预测错误的原因。结论:本研究发现,模型必须有足够的能力来检测多种风险因素。MRFR 性能下降的主要原因是难以识别视觉模糊性,以及在涉及透视时倾向于关注附近的物体。实际应用:本研究通过开发一种识别多标签物质和人为风险因素的模型,为安全管理知识做出了贡献。此外,研究结果还可作为今后数据收集方法和模型改进的指导方针。MRFR 框架可作为一种算法,在现实世界的建筑工地上预先自动识别风险因素。
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来源期刊
CiteScore
6.40
自引率
4.90%
发文量
174
审稿时长
61 days
期刊介绍: Journal of Safety Research is an interdisciplinary publication that provides for the exchange of ideas and scientific evidence capturing studies through research in all areas of safety and health, including traffic, workplace, home, and community. This forum invites research using rigorous methodologies, encourages translational research, and engages the global scientific community through various partnerships (e.g., this outreach includes highlighting some of the latest findings from the U.S. Centers for Disease Control and Prevention).
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