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Understanding near-miss count data on construction sites using greedy D-vine copula marginal regression

机译：使用贪婪的D-VINE Copula边缘回归了解近乎错过的施工网站数量

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摘要

Near misses are an important type of accident precursor because they provide insights into failure-generating mechanisms, help understand safety risks, and guide necessary interventions before they develop into real accidents. A high occurrence frequency of near-miss incidents typically signals a warning of small safety margins. However, the occurrences of near-miss incidents are stochastic and serially dependent in nature. Ignoring these features will typically lead to a misunderstanding of a project's safety level. This paper presents a D-vine copula marginal regression model for count time series data. Incident counts are expressed as a function of predictors. A time-varying discrete marginal distribution is used to describe the uncertainty of incident occurrences at an arbitrary time point, while the dependence between consecutive observations at different time points is captured with copula functions. To allow for long-range and non-Gaussian dependences between incidents, the D-vine structure is used to build the multivariate copula function where the bivariate copula associated with each edge is not necessarily Gaussian. To avoid evaluating a large number of candidate models that use different marginals and bivariate copulas as building blocks, a greedy algorithm is proposed to estimate relevant parameters and select the best model. An information criterion is used to determine the tree level of the D-vine decomposition, which implies the Markov structure of the incident occurrences. The proposed method is applied to a set of near-miss count data collected from a construction project over 5 years. We show that the hidden dependence has a relatively large time-lag and is strongly non-Gaussian. Comparison with conventional methods shows that the proposed method is efficient and yields better predictive performance.
机译：近期未命中是一种重要的事故前体，因为它们提供了对失败产生机制的见解，有助于了解安全风险，并在发展到真正的事故之前指导必要的干预措施。近误小姐事件的高出现频率通常会发出小安全边缘的警告。然而，近乎错过的事件发生的发生是随机性的，并且在性质上依据。忽略这些功能通常会导致对项目的安全水平的误解。本文介绍了计数时间序列数据的D-VINE Copula边缘回归模型。事件计数表示为预测器的函数。使用时间变化的离散边缘分布来描述任意时间点在任意时间点处发生的事件的不确定性，而在Copula函数中捕获不同时间点的连续观测之间的依赖性。为了允许事故之间的远程和非高斯依赖性，D-VINE结构用于构建与每个边缘相关联的双变共拷贝的多变量Copula功能不一定是高斯的。为避免评估使用不同边缘和双变共用的大量候选模型作为构建块，提出了一种贪婪的算法来估计相关参数并选择最佳模型。信息标准用于确定D-VINE分解的树级，这意味着事件发生的Markov结构。该提出的方法应用于从5年超过建筑项目收集的一组近似小姐的计数数据。我们表明隐藏的依赖性具有相对较大的时间滞后，并且是强大的非高斯的。与传统方法的比较表明，该方法是有效的，产生更好的预测性能。

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  • 来源
    《Reliability Engineering & System Safety》 |2021年第9期|107687.1-107687.12|共12页
  • 作者

    Wang Fan; Li Heng; Dong Chao;

  • 作者单位

    Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Peoples R China|Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China;

    Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China;

    Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Construction safety; Near-miss; Probabilistic model; Count data; D-vine copula;

    机译：建筑安全;近乎遗漏;概率模型;计数数据;D-vine copula;

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