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Modeling the effects of production pressure on safety performance in construction projects using system dynamics

机译:使用系统动力学模拟生产压力对建设项目安全绩效的影响

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Introduction: Construction incidents occur due to system failures, not due to a single factor such as unsafe behavior or condition. Therefore, construction safety should be investigated using a systematic view capable of illustrating the complex nature of incidents. Construction projects are also often behind their planned schedule and suffer from various pressures caused by contractual deadlines or clients. Previous studies demonstrated that such pressures negatively affect safety performance; however, the process of how production pressure influences safety performance is not fully investigated. Method: The present research aimed to understand the feedback mechanism of how production pressure interactively affects safety performance and safety-related managerial components in a construction project. Ground theory method (GTM) is used to create a conceptual causal loop diagram that shows the relationship between incident rate and other variables such as labor hour, actual and planned progress, safety climate, rework, and safety training. Moreover, a power plant construction project was used as a case study to practically investigate the conceptual model; a case study is employed to build a System Dynamics (SD) model. The simulation model was then validated using behavior reproduction and sensitivity analysis. Results: The results of the inequality statistics show that the simulation model can be used to forecast trends in the incident rate. (C) 2019 National Safety Council and Elsevier Ltd. All rights reserved.
机译:简介:施工事件的发生是由于系统故障,而不是由于诸如不安全行为或状况之类的单一因素引起的。因此,应使用能够说明事件复杂性的系统视图来研究施工安全。建设项目通常也落后于计划的时间表,并且由于合同期限或客户而承受各种压力。先前的研究表明,这种压力会对安全性能产生负面影响。但是,尚未全面研究生产压力如何影响安全性能的过程。方法:本研究旨在了解生产压力如何交互影响建筑项目中的安全绩效和与安全相关的管理组件的反馈机制。地面理论方法(GTM)用于创建概念上的因果关系图,该图显示事故发生率与其他变量(如工时,实际和计划的进度,安全气候,返工和安全培训)之间的关系。此外,以电厂建设项目为例,对概念模型进行了实际研究。通过案例研究来构建系统动力学(SD)模型。然后使用行为再现和敏感性分析验证仿真模型。结果:不平等统计的结果表明,该仿真模型可用于预测事件发生率的趋势。 (C)2019国家安全委员会和Elsevier Ltd.保留所有权利。

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