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首页> 外文期刊>WSEAS Transactions on Business and Economics >Risk analysis in tunnel construction with Bayesian networks using mutual information for safety policy decisions
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Risk analysis in tunnel construction with Bayesian networks using mutual information for safety policy decisions

机译：贝叶斯网络隧道施工风险分析，贝叶斯网络使用相互信息安全政策决策

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摘要

Tunnel construction is affected from its origins by different types of uncertainties responsible for innumerable safety risks. This problem has been addressed constantly during the last times achieving positive results, but the complex work scenarios and the common variability of the construction processes prevent putting an end to this problem. For this reason, this study presents an alternative methodology for safety prioritization in tunnel construction gaining relevant information hitherto unknown which can be crucial for policy making in infrastructure projects. The method proposed consists on the Bayesian analysis of data from occupational accidents recorded during the construction of tunnels in the last years. For this purpose, the model variables are rigorously estimated from expert judgement supported by the analysis of data from previous projects. Once the bayesian model is built, the dependencies among the variables are examined using the mutual information. The results obtained from the mutual information analysis allow to detect the main risks responsible for the occurrence of accidents and how they interact. Afterwards, a simplified Bayesian model with the most relevant risk factors affecting safety is built. Through the bayesian inference process, this condensed and validated model facilitates the exploration of significant contributions for safety policy decisions in tunnel construction. Overall, the results obtained provide a deep insight about the most influential factors on which should be focus the efforts to reduce accidents. Several safety risk factors are further influenced by human and organizational factors, whose effect can be reduced in advance. The mechanism of risk migration was better understood when analysing the interaction between the variables in the Bayesian model. In general, the accurate simplification of the model network demonstrated to be a powerful tool to comprehend the uncertainty associated to complex problems.
机译：隧道施工受到不同类型的不确定性的起源影响，负责无数安全风险。在最后一次实现积极结果期间，在最后一次逐渐解决了这个问题，但复杂的工作场景和施工过程的常见变化可以防止结束这个问题。因此，本研究提出了隧道施工安全优先级的替代方法，获得了迄今为止未知的相关信息，这对于基础设施项目的政策制定至关重要。该方法提出的是，在过去几年在隧道建设期间记录的职业事故数据的贝叶斯人分析。为此目的，模型变量经过先前项目的数据分析支持的专家判断严格估计。构建贝叶斯模型后，使用相互信息检查变量之间的依赖关系。从互信息分析中获得的结果允许检测负责事故发生以及它们的互动的主要风险。之后，建立了一种简化的贝叶斯模型，具有影响安全性最相关的风险因素。通过贝叶斯推理过程，这种浓缩和验证的模型有助于探索隧道建设中的安全政策决策的重大贡献。总体而言，获得的结果对最有影响力的因素提供了深入的洞察力，这些因素应该集中努力减少事故。有几种安全危险因素受到人类和组织因素的进一步影响，其效果可以提前减少。在分析贝叶斯模型中的变量与变量之间的相互作用时，更好地理解风险迁移机制。通常，模型网络的准确简化表明是一种强大的工具，可以理解与复杂问题相关的不确定性。

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