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Construction Safety Risk Modeling and Simulation

机译：施工安全风险建模与仿真

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By building on a genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data-driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute-based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency-magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state-of-the-art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety-related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.
机译：通过基于遗传启发的基于属性的安全风险分析概念框架，我们提出了一种新的方法来定义，建模和模拟情景级别的单变量和双变量施工安全风险。我们完全由数据驱动的技术为建筑从业人员和院士提供了一种简便，自动化的方法，可从非结构化文本损伤报告中提取的基于属性的数据中获得宝贵的经验见解。通过将我们的方法应用于814个伤害报告的数据集，我们首先显示出建筑安全风险的频率幅度分布与许多自然现象，例如降雨或地震，非常相似。受此观察结果的启发，并借鉴水文气候学和保险领域的最新技术，然后基于核密度估计量和copulas介绍单变量和双变量非参数随机安全风险生成器。这些生成器使用户能够生成忠实于原始数据的大量综合安全风险值，从而使不确定性下的安全相关决策可以基于大量的经验证据。我们研究的意义之一是，与自然现象一样，可以通过利用经验数据进行定量研究，而不是通过使用主观数据（目前的行业标准）从管理角度严格地研究建筑安全，从而从中受益。最后，还有一个有趣的发现是，在我们的数据集中，与高能量水平（例如，机械，有害物质）和人为错误（例如，工具的安全性不当）相关的属性逐渐成为强大的风险塑造者。

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