安全科学与韧性(英文)最新文献_第4页

Guidance for estimating the blast load from vapour cloud explosions in traffic environments using the multi-energy method 用多能法估计交通环境中蒸汽云爆炸爆炸载荷的导则

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-06-22 DOI: 10.1016/j.jnlssr.2025.100222

Fabio Lozano , Morgan Johansson , Joosef Leppänen , Mario Plos

The accidental release of a flammable gas on a road can result in a vapour cloud explosion (VCE). Such VCEs generate a blast wave that propagates away from the explosion, potentially damaging nearby structures. The TNO Multi-Energy Method is commonly used for a simplified estimate of the blast load resulting from a VCE. The method characterises the severity and duration of the blast wave using a case-specific strength class and combustion energy (which the method relates to the gas volume of the equivalent blast source). However, no specific guidelines for estimating the strength class in urban roads or related settings (such as carparks) are currently available in the literature. This makes implementing the method in such scenarios challenging and imprecise. The authors’ work used computational fluid dynamics (CFD) to evaluate multiple gas explosion scenarios and proposed recommendations for determining the strength class and gas volume at the blast source. These scenarios comprised a group of vehicles engulfed by a stoichiometric propane-air cloud. It was concluded that the strength class could be reasonably estimated based on the number of vehicles in the transverse direction. Furthermore, the guidance for estimating the gas volume at the equivalent blast source was based on the critical gas volume, after which no further enhancement of overpressure was obtained. The recommendations were implemented in several scenarios and compared with corresponding CFD analyses. The results showed very good agreement for predicting impulse. Predicting overpressure was affected by the inherent asymmetry of the scenarios, although it was possible to achieve acceptable and conservative results.

可燃气体在道路上的意外释放会导致蒸汽云爆炸（VCE）。这种vce产生的冲击波会从爆炸处传播出去，可能会破坏附近的建筑物。TNO多能法通常用于对VCE产生的爆炸载荷的简化估计。该方法使用特定情况的强度等级和燃烧能量（该方法与等效爆炸源的气体体积有关）来表征爆炸波的严重性和持续时间。然而，目前文献中没有具体的指导方针来估计城市道路或相关环境（如停车场）的强度等级。这使得在这种情况下实现该方法具有挑战性且不精确。作者的工作使用计算流体动力学（CFD）来评估多种气体爆炸情景，并提出了确定爆炸源强度等级和气体体积的建议。这些场景包括一组被化学计量丙烷空气云吞没的车辆。结果表明，可以根据横向车辆的数量合理地估计其强度等级。在等效爆炸源处，气体体积的估计以临界气体体积为指导，超过临界气体体积后，超压没有进一步提高。这些建议在几个场景中实施，并与相应的CFD分析进行了比较。结果表明，对冲量的预测具有很好的一致性。尽管有可能获得可接受的、保守的结果，但超压预测受到情景固有的不对称性的影响。

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引用次数: 0

CO-Mode with AV-CRM: A novel paradigm towards human–machine collaboration in intelligent vehicle safety 基于AV-CRM的协同模式：智能车辆安全中人机协作的新范式

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-06-17 DOI: 10.1016/j.jnlssr.2025.04.002

Jiayi Lu , Bin Sun , Boao Zhang, Zhaowen Pang, Zhaoxia Peng, Shichun Yang, Yaoguang Cao

With the continuous advancement in vehicle intelligence, enhancing safety has emerged as a key priority in intelligent vehicle research and development. Intelligent vehicles are currently limited in supporting autonomous or human-driven modes. This limitation becomes apparent in complex driving scenarios, where vehicle risk response capabilities are inadequate. This paper suggests that shifting from a single decision maker to a human–machine collaboration approach is a potential solution. However, current research on human–machine collaboration in intelligent vehicles primarily focuses on intelligent systems that assist the driver, rather than treating the driver and the system as equals. This approach overlooks the role of the driver in helping the system, lacks effective communication, and diminishes the sense of collaborative cooperation, all of which hinder the promotion of efficient, safe, and stable driving. Inspired by the aviation approach to risk management through Crew Resource Management (CRM), this study introduces the Collaborative Operation Mode (CO-Mode) for intelligent vehicles. Based on CO-Mode’s requirements for human–machine collaborative perception, decision making, and control, the Autonomous Vehicle Collaborative Resource Management (AV-CRM) is proposed. In addition, challenges and future perspectives are explored by analyzing the abilities and limitations of relevant technologies. The proposed AV-CRM redefines the relationship between drivers and intelligent systems, offering new insights into the safety of intelligent vehicles and their technological evolution.

随着汽车智能化水平的不断提高，提高安全性已成为智能汽车研发的重点。目前，智能汽车在支持自动驾驶或人类驾驶模式方面受到限制。在车辆风险响应能力不足的复杂驾驶场景中，这种限制变得明显。本文认为，从单一决策者转向人机协作方法是一种潜在的解决方案。然而，目前对智能汽车人机协作的研究主要集中在辅助驾驶员的智能系统上，而不是将驾驶员和系统平等对待。这种方式忽视了驾驶员帮助系统的作用，缺乏有效的沟通，削弱了协同合作的意识，这些都阻碍了高效、安全、稳定驾驶的促进。本研究受航空风险管理方法（Crew Resource management， CRM）的启发，引入智能交通工具的协同运作模式（CO-Mode）。基于CO-Mode对人机协同感知、决策和控制的要求，提出了自动驾驶汽车协同资源管理（AV-CRM）。此外，通过分析相关技术的能力和局限性，探讨了挑战和未来的前景。提出的AV-CRM重新定义了驾驶员与智能系统之间的关系，为智能车辆的安全性及其技术发展提供了新的见解。

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引用次数: 0

Extending the STPA to model the control structure with Finite State Machine 将STPA扩展到有限状态机控制结构的建模

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-06-11 DOI: 10.1016/j.jnlssr.2025.04.004

Tiago Aroeira Marliere, Cecilia de Azevedo Castro Cesar, Celso Massaki Hirata

Cyber–physical systems (CPSs) are becoming increasingly complex, integrating physical entities with diverse computing and communication resources, multiple processors, networks, and devices. One example is the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) system, where interactions among components can lead to UAS collisions and harm to people and property. System Theoretic Process Analysis (STPA) is a systems theory-based technique for conducting early-stage safety analyses of complex systems. The Model the Control Structure step in STPA involves identifying each controller component, its process models, and its control actions. However, conventional STPA process models use only variables and states, which may be insufficient for systems involving entities that transition through multiple state flows. This study introduces a novel extension by integrating Finite State Machine (FSM) modeling into the Model the Control Structure step. The FSM-based approach captures detailed behaviors of entities requiring control by explicitly modeling their states and transitions in an iterative process. This extended STPA was applied to the UTM to control the delivery of UAV packages. The results demonstrate that the FSM extension enhances identifying control actions, feedback loops, process model variables, and unsafe control actions. The study concludes that the extended STPA provides a systematic approach for analyzing CPSs with entities that undergo complex state transitions, contributing to improved systematization and consistency of safety analyses.

信息物理系统（cps）正变得越来越复杂，它将物理实体与各种计算和通信资源、多处理器、网络和设备集成在一起。一个例子是无人机系统（UAS）交通管理（UTM）系统，其中组件之间的相互作用可能导致UAS碰撞并对人员和财产造成伤害。系统理论过程分析（STPA）是一种基于系统理论的复杂系统早期安全分析技术。STPA中的“为控制结构建模”步骤涉及识别每个控制器组件、其过程模型及其控制动作。然而，传统的STPA流程模型仅使用变量和状态，这对于涉及通过多个状态流转换的实体的系统可能是不够的。本文通过将有限状态机（FSM）建模集成到模型控制结构步骤中，提出了一种新的扩展方法。基于fsm的方法通过在迭代过程中显式地建模其状态和转换来捕获需要控制的实体的详细行为。该扩展的STPA应用于UTM来控制无人机包裹的递送。结果表明，FSM扩展增强了对控制动作、反馈回路、过程模型变量和不安全控制动作的识别能力。研究表明，扩展的STPA为分析具有复杂状态转换的实体的cps提供了一种系统方法，有助于提高安全性分析的系统化和一致性。

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引用次数: 0

A deep learning and edge computing integrated approach for fall behavior detection in buildings 基于深度学习和边缘计算的建筑物坠落行为检测方法

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-06-11 DOI: 10.1016/j.jnlssr.2025.100218

Mingze Ma , Xiaofeng Hu

This study proposes a model integrating YOLOv11 and STGCN for accurate and real-time detection of fall behaviors within buildings. A specialized video dataset comprising fall behaviors performed by six volunteers was developed and used to validate the model’s effectiveness in cloud computing and edge computing environments. The results obtained in the cloud computing environment were characterized by ample computational resources and the absence of real-time constraints. The model achieved precision, recall, and F1-score for fall behaviors exceeding 0.98. The model was integrated into edge computing devices in an actual test environment to directly process real-time video stream data. A missed detection rate of 18 % was observed on the Jetson ORIN NX 16GB device, while the Jetson AGX Orin 64GB recorded a lower missed detection rate of 15 %. Similarly, a false alarm rate of 16 % was observed on the Jetson ORIN NX 16GB device and 12 % on the Jetson AGX Orin 64GB device. These performance differences between the high-performance cloud computing cluster and edge computing devices, as well as among different edge computing devices, may be attributed to variations in computational resources, data quality, and device parameters. The results demonstrate the potential of the proposed model for real-time fall detection in resource-constrained environments.

本研究提出了一个整合YOLOv11和STGCN的模型，用于准确实时地检测建筑物内的坠落行为。开发了包含六名志愿者摔倒行为的专门视频数据集，并用于验证该模型在云计算和边缘计算环境中的有效性。在云计算环境中获得的结果具有计算资源充足和没有实时约束的特点。该模型对跌倒行为的查全率、查全率和f1得分均超过0.98。在实际测试环境中，将该模型集成到边缘计算设备中，直接处理实时视频流数据。Jetson ORIN NX 16GB设备的漏检率为18%，而Jetson AGX ORIN 64GB设备的漏检率较低，为15%。同样，在Jetson ORIN NX 16GB设备上观察到的误报率为16%，在Jetson AGX ORIN 64GB设备上观察到的误报率为12%。高性能云计算集群和边缘计算设备之间以及不同边缘计算设备之间的这些性能差异可能归因于计算资源、数据质量和设备参数的差异。结果证明了该模型在资源受限环境下进行实时跌倒检测的潜力。

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引用次数: 0

Systematic literature review of Ergonomic evaluation methods in the mining sector (2015-2024) 采矿业人机工程学评价方法系统文献综述（2015-2024）

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-06-06 DOI: 10.1016/j.jnlssr.2025.100215

Nelson Chambi Quiroz , David Mauricio , Jorge Inche Mitma , Celso Sanga

Musculoskeletal disorders (MSDs) in the mining sector are frequently associated with exposure to ergonomic risk factors, leading to decreased productivity and increased absenteeism. This study presents a systematic literature review of ergonomic evaluation methods, identified risk factors, and affected body regions, based on journal articles indexed in ScienceDirect, Emerald, Taylor & Francis, Wiley, Scopus, Sage, IEEE Xplore, and Web of Science. From an initial pool of 213 studies, 49 primary studies were selected, 82 % of which were published in Q1 or Q2 journals. The review identifies six individual ergonomic assessment methods (Nordic Questionnaire, ISO/IEC 2631–1, REBA, OWAS, NIOSH, and Risk Score) and two hybrid methods (Bayesian Network + REBA, and RULA + Nordic Questionnaire). Additionally, it categorizes 13 affected body parts (e.g., back, shoulders, neck, waist) and 11 ergonomic factors (e.g., heavy loads, repetitive tasks, vibration, static postures, and work schedules). The findings reveal that a majority of studies focus on underground mining operations, with the Nordic Questionnaire and ISO/IEC 2631–1 being the most frequently used methods. The back and shoulders are the most commonly studied body parts, while vibration and working hours emerge as the most prevalent risk factors. Finally, six challenges are proposed to address current research gaps, including the integration of deep learning techniques and the evaluation of less-studied joints such as the wrist and elbow. This review provides a valuable foundation for researchers and mine safety professionals seeking to advance ergonomic assessment in mining environments.

采矿部门的肌肉骨骼疾病（MSDs）往往与接触人体工程学风险因素有关，导致生产力下降和缺勤率增加。本研究对人体工程学评估方法、确定的危险因素和受影响的身体部位进行了系统的文献综述，基于ScienceDirect、Emerald、Taylor &；Francis, Wiley, Scopus, Sage, IEEE explore, and Web of Science。从最初的213项研究中，选择了49项主要研究，其中82%发表在Q1或Q2期刊上。该综述确定了六种单独的人体工程学评估方法（北欧问卷、ISO/IEC 2631-1、REBA、OWAS、NIOSH和风险评分）和两种混合方法（贝叶斯网络+ REBA和RULA +北欧问卷）。此外，它还对13个受影响的身体部位（如背部、肩部、颈部、腰部）和11个人体工程学因素（如重负荷、重复任务、振动、静态姿势和工作时间表）进行了分类。调查结果显示，大多数研究集中于地下采矿作业，北欧调查表和ISO/IEC 2631-1是最常用的方法。背部和肩部是最常被研究的身体部位，而振动和工作时间是最普遍的危险因素。最后，提出了解决当前研究空白的六个挑战，包括深度学习技术的集成和对腕部和肘部等较少研究的关节的评估。这一综述为研究人员和矿山安全专业人员寻求在矿山环境中推进人体工程学评估提供了有价值的基础。

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引用次数: 0

Resilience assessment of public health system at multi-levels: An emergency management capacity quantification model for pandemic response 多层次公共卫生系统弹性评估：大流行应急管理能力量化模型

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-06-06 DOI: 10.1016/j.jnlssr.2025.100220

Qing Deng , Yanchao Ye , Wei Wang , Hui Zhang

Facing the global pandemic of coronavirus disease 2019 (COVID-19), countries and regions have implemented different policies and non-pharmacological interventions (NPIs) according to their circumstances. These policies and intervention measures provide new insights into assessing local resilience from the perspective of response capacities in the public health system. This study aims to establish a multi-dimensional and dynamic resilience assessment model based on the index system method. The complete assessment model includes building a comprehensive system, executing the system in specific scenarios, and measuring resilience. The comprehensive system, like a guideline, is constructed from six key categories. The system involves the entire society, encompassing various levels, including country, state, province, city, local community, and individual. It considers not only policy formulation but also the actual implementation of the policy. The comprehensive system does not necessarily apply to all scenarios during the system's implementation. The actual case, the prevention & control in England, is introduced to assess the local resilience and verify the proposed assessment model. The results prove that our model can be used to assess local resilience for the public health system and seek capacity improvement when responding to epidemic situations.

面对2019冠状病毒病（COVID-19）全球大流行，各国和地区根据自身情况实施了不同的政策和非药物干预措施。这些政策和干预措施为从公共卫生系统应对能力的角度评估当地恢复力提供了新的见解。本研究旨在建立基于指标体系方法的多维动态弹性评价模型。完整的评估模型包括构建一个全面的系统，在特定的场景中执行系统，以及测量弹性。综合体系就像一个指南，由六个关键类别构成。该制度涉及整个社会，包括国家、州、省、市、地方社区和个人等各个层面。它不仅考虑政策的制定，而且考虑政策的实际执行。全面的系统并不一定适用于系统实施过程中的所有场景。实际案例，预防&；，以评估当地的恢复力，并验证所提出的评估模型。结果证明，我们的模型可以用来评估当地公共卫生系统的弹性，并在应对疫情时寻求能力改进。

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引用次数: 0

Study on emergency evacuation guidance in the subway station hall 地铁车站大厅应急疏散引导研究

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-06-06 DOI: 10.1016/j.jnlssr.2025.100216

Ping Zhang , Yujie Cui , Lizhong Yang , Kaixuan Wang , Wenjun Liu , Jinzhong Wu

In recent years, with continued urbanization, subway stations with complex structures have developed rapidly, posing serious challenges to daily crowd management and emergency evacuation safety. A social force-based evacuation guidance model was developed to simulate the process of pedestrian evacuation at the concourse level of a subway station in Shenyang by combining questionnaires and field surveys. The objective was to explore the impact of various factors—such as the viewing distance, guidance number, guidance location, and proportion of pedestrians accepting the guiding information—on evacuation efficiency in subway station halls with multiple exits. The results showed that static leaders reduced the evacuation time, especially in situations with a limited field of view. Compared with the “no guidance” case, the evacuation time was shortened by approximately 20.16 % and 9 % when the viewing distances were 2 and 6 m, respectively. Moreover, a small number of static leaders could effectively guide the crowd to evacuate the subway station hall, demonstrating the positive role of guidance in emergency evacuations. Considering the actual situation and human cost, it was reasonable to have eight static leaders in the subway station hall, which reduced the evacuation time by approximately 40.47 % compared to the situation without guidance. Influenced by the viewing distance, scene layout, and pedestrian density, when static leaders were distributed at the exits, evacuation improved. Moreover, the higher the percentage of pedestrians accepting the guidance information, the better the evacuation performance. This study provides scientific support for guidance arrangements in the daily management and emergency evacuation of subway stations.

近年来，随着城市化进程的不断推进，结构复杂的地铁车站发展迅速，对日常人群管理和应急疏散安全提出了严峻的挑战。采用问卷调查和现场调查相结合的方法，建立了基于社会力量的疏散引导模型，对沈阳市某地铁车站大堂层行人疏散过程进行了模拟。本研究的目的是探讨观看距离、引导数量、引导位置和接受引导信息的行人比例等因素对多出口地铁站大厅疏散效率的影响。结果表明，静态先导减少了疏散时间，特别是在视野有限的情况下。与无引导情况相比，视距为2 m和6 m时，疏散时间分别缩短了约20.16%和9%。此外，少量的静态引导者可以有效地引导人群疏散地铁站大厅，显示了引导在紧急疏散中的积极作用。考虑到实际情况和人力成本，在地铁站大厅设置8个静态引导员是合理的，与没有引导员的情况相比，可以减少约40.47%的疏散时间。受观景距离、场景布局和行人密度等因素的影响，在出口处设置静态引路人有利于疏散。此外，行人接受引导信息的比例越高，疏散效果越好。本研究为地铁车站日常管理和应急疏散的引导安排提供了科学依据。

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引用次数: 0

Association analysis of causative factors of fall from height accidents 高空坠落事故成因的关联分析

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-06-06 DOI: 10.1016/j.jnlssr.2025.100221

Hanjun Guo , Yuwei Mo , Zixi Wang , Rongxue Kang , Ke Tang , Qiuju Ma

Working at height is widespread across various industries, with frequent and hazardous falls occurring regularly. Such tasks are often linked to multifactorial issues, where the interplay of diverse factors leads to accidents that are challenging to control effectively. This study establishes an index system for the factors influencing falls from height by statistically analyzing 101 incidents, identifying 64 causative elements classified into four categories. These include 17 factors related to operator condition and behavior, 13 concerning equipment and facility conditions, 7 pertaining to site conditions, and 27 associated with production operations management. Utilizing the Apriori algorithm and Gephi software, the study mined the association rules of causal factors in falls from height and constructed their network diagram. By examining association rules with high support, confidence, and lift, the relationships between key causal factors leading to accidents are clarified, identifying critical operational control points and providing a scientific foundation for reducing the incidence of falls from height. Currently, China's standards related to working at height remain fragmented. This study lays the foundation for the development of comprehensive, systematic, generic safety management standards for working at height, satisfying the needs of the field.

高空作业在各行各业都很普遍，经常发生危险的坠落事故。这些任务通常与多因素问题有关，其中各种因素的相互作用导致难以有效控制的事故。本研究通过对101起事故的统计分析，确定了64个导致坠楼的因素，并将其分为4类，建立了影响坠楼因素的指标体系。这些因素包括17个与操作人员状态和行为有关的因素，13个与设备和设施条件有关，7个与现场条件有关，27个与生产运营管理有关。利用Apriori算法和Gephi软件，挖掘了高空坠落事件中各因果因素的关联规律，并构建了因果关系网络图。通过对高支撑、高置信度、高升力关联规则的研究，明确了导致事故的关键原因之间的关系，确定了关键的操作控制点，为减少高空坠落事故的发生提供了科学依据。目前，中国与高空作业相关的标准仍然支离破碎。本研究为制定全面、系统、通用的高空作业安全管理标准奠定了基础，满足了该领域的需求。

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引用次数: 0

An analysis of relationships between occupational injury, job satisfaction and turnover intention among stone, sand, and gravel mine workers 石砂砂石矿山工人职业伤害、工作满意度与离职倾向的关系分析

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-06-06 DOI: 10.1016/j.jnlssr.2025.100219

Todd D. Smith, Kiran Mondal

Stone, sand, and gravel mining accounts for 80 % of all mining operations. Maintaining a skilled labor force is essential for this large and growing mining sector. Occupational injuries negatively impact worker health and well-being and are costly. Additionally, injuries may impact workers’ job satisfaction and their desire to seek other employment. The impact of work-related occupational injuries on job satisfaction and turnover intention has not been explored in stone, sand, and gravel mining operations. This study analyzed cross-sectional data from 459 workers employed in the stone, sand, and gravel mining industry in the midwestern United States. Mplus was used to complete path analysis to assess a hypothesized model and its relationships. Analyses determined the model was a good fit for the data, occupational injuries negatively impacted worker job satisfaction, job satisfaction negatively impacted turnover intention, and job satisfaction mediated the relationship between occupational injuries and turnover intention. These findings confirm posited hypotheses and provide evidence that occupational injuries not only harm workers but also result in diminished job satisfaction and ultimately turnover intention, important business outcomes for the stone, sand, and gravel mining industry.

石头、沙子和砾石开采占所有采矿作业的80%。对于这个庞大且不断增长的采矿业来说，保持熟练的劳动力至关重要。职业伤害对工人的健康和福祉产生负面影响，而且代价高昂。此外，伤害可能会影响工人的工作满意度和他们寻找其他工作的愿望。与工作有关的职业伤害对工作满意度和离职意愿的影响尚未在石料、砂石和砾石开采作业中进行研究。这项研究分析了美国中西部459名从事石头、沙子和砾石开采的工人的横截面数据。使用Mplus完成路径分析以评估假设模型及其关系。分析表明，模型与数据拟合良好，职业伤害负向影响员工工作满意度，工作满意度负向影响离职倾向，工作满意度在职业伤害与离职倾向之间起中介作用。这些发现证实了假设，并提供证据表明，职业伤害不仅伤害工人，而且导致工作满意度降低，最终导致离职意向，这是石砂和砾石采矿业的重要业务成果。

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引用次数: 0

The value of structured occupational safety data and cluster analysis: A case study from the Italian National Surveillance System 结构化职业安全数据和聚类分析的价值：来自意大利国家监测系统的案例研究

IF 3.4 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

安全科学与韧性(英文)

Pub Date : 2025-05-22 DOI: 10.1016/j.jnlssr.2025.03.003

Armando Guglielmi , Antonio Leva , Mauro Pellicci , Maria Grazia Gnoni , Fabiana Tornese

The collection and analysis of accident data are crucial steps towards the application of effective preventive measures in the occupational safety context. The analysis of work-related accidents/injuries is a focal starting point for improving the safety level in each sector. Data collection represents a critical activity in this field since it is not characterized by a standardized approach. Quantitative models allowing the extraction of root causes of an injury can be effectively applied without a vast computational effort only if structured safety data are available. The aim of this study is to demonstrate the benefits related to the adoption of a structured and standardized model for the collection and analysis of safety data. Thus, the application of quantitative methods (based on statistical models) to structured safety data about injuries is proposed in the present study. A case study on fatal injuries that occurred in the construction sector in Italy from 2002 to 2022 is discussed, based on data collected through a structured database called Infor.MO and managed by the Italian National Institute for Insurance against Accidents at Work (INAIL), Regions and Autonomous Provinces and Local Health & Safety Departments (LHSDs). A statistical analysis based on a multi-step process (including optimal scaling, principal component analysis, and cluster analysis) has been applied to point out the real accident scenarios and root causes characterizing the sector analyzed. Thanks to the structured set of data provided by Infor.MO, applying different statistical techniques has been supported, elaborating data and extracting meaningful information about the most significant fatal injury scenarios. The results obtained allowed us to point out four main clusters of fatal injury scenarios, highlighting common causes reliably. This application has pointed out operational and strategic benefits related to the availability of structured safety data to both highlight root causes of injuries and, in a more strategic way, to improve the effectiveness of prevention activities.

收集和分析事故数据是在职业安全方面采取有效预防措施的关键步骤。对工伤事故/伤害的分析是提高每个部门安全水平的重点起点。数据收集是这一领域的一项关键活动，因为它没有标准化的方法。只有在结构化的安全数据可用的情况下，才能有效地应用定量模型，提取伤害的根本原因，而无需大量的计算工作。本研究的目的是证明采用结构化和标准化模型收集和分析安全数据的好处。因此，本研究提出将定量方法（基于统计模型）应用于结构化伤害安全数据。本文基于结构化数据库Infor收集的数据，对2002年至2022年意大利建筑行业发生的致命伤害进行了案例研究。MO，由意大利国家工伤事故保险协会、各地区和自治省以及地方卫生部门管理；安全部门（LHSDs）。应用基于多步骤过程（包括最优缩放、主成分分析和聚类分析）的统计分析，指出了所分析行业的真实事故场景和根本原因。多亏了Infor提供的结构化数据集。MO，应用不同的统计技术已得到支持，详细数据和提取有关最重要的致命伤害情景的有意义的信息。获得的结果使我们能够指出四种主要的致命伤害情景，突出了常见的原因。该应用程序指出了与结构化安全数据的可用性相关的操作和战略效益，这些数据既突出了伤害的根本原因，又以更具战略意义的方式提高了预防活动的有效性。

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引用次数: 0