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Capturing and Integrating Knowledge for Managing Risks in Tunnel Works

机译:收集和整合知识以管理隧道工程中的风险

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摘要

Risk-related knowledge gained from past construction projects is regarded as potentially extremely useful in risk management. This article describes a proposed approach to capture and integrate risk-related knowledge to support decision making in construction projects. To ameliorate the problem related to the scarcity of risks information often encountered in construction projects, Bayesian Belief Networks are used and expert judgment is elicited to augment available information. Particularly, the article provides an overview of judgment-based biases that can appear in the elicitation of judgments for constructing Bayesian Networks and the provisos that can be made in this respect to minimize these types of bias. The proposed approach is successfully applied to develop six models for top risks in tunnel works. More than 30 tunneling experts in the Netherlands and Germany were involved in the investigation to provide information on identifying relevant scenarios than can lead to failure events associated with tunneling risks. The article has provided an illustration of the applicability of the developed approach for the case of "face instability in soft soils using slurry shields."
机译:从过去的建设项目中获得的与风险相关的知识在风险管理中被认为具有极大的潜力。本文介绍了一种建议的方法来捕获和集成与风险相关的知识,以支持建筑项目中的决策。为了缓解与在建设项目中经常遇到的风险信息稀缺有关的问题,使用了贝叶斯信念网络,并通过专家判断来增加可用信息。特别是,本文提供了基于判断的偏见的概述,这些偏见可能会出现在对构建贝叶斯网络的判断的启发中,并且可以在这方面做出使这些偏见最小化的条件。所提出的方法已成功地应用于开发隧道工程中最高风险的六个模型。荷兰和德国的30多位隧道专家参与了调查,以提供有关识别相关场景的信息,这些场景可能导致与隧道风险相关的失败事件。这篇文章说明了开发的方法在“使用泥浆护罩的软土中的表面不稳定”情况下的适用性。

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