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  • 标题:Exploring Technical Decision-Making Risks in Construction Megaprojects Using Grounded Theory and System Dynamics
  • 本地全文:下载
  • 作者:Xiaoying Tang ; Mengjun Wang ; Qian Wang
  • 期刊名称:Computational Intelligence and Neuroscience
  • 印刷版ISSN:1687-5265
  • 电子版ISSN:1687-5273
  • 出版年度:2022
  • 卷号:2022
  • DOI:10.1155/2022/9598781
  • 语种:English
  • 出版社:Hindawi Publishing Corporation
  • 摘要:Technical decision-makings (TDMs) are a vital part of the decision-makings in construction megaprojects, facing high risks brought by technical complexity, dynamic environment, and subject cognition. Identifying technical decision-making risks (TDMRs) and exploring their interactions are important in megaproject management. Due to the high complexity of TDMs in megaprojects, TDMRs are complex and diverse. However, there is a lack of research on exploring the systematic TDMRs in megaprojects. To address this gap in knowledge, this paper aims to better understand the dynamic complexity of TDMRs in megaprojects by identifying the risks and exploring their interactions from a dynamic and systematic perspective. Grounded theory (GT) and system dynamics (SD) were adopted for this research. First, the GT was used to identify TDMRs in megaprojects and create a conceptual model depicting the relationships among TDMRs. Then, an SD model characterizing the causal structure of the TDMRs system in megaprojects is developed in both qualitative and quantitative manners. The developed model involves interrelationships among environmental risks, decision-making process risks, and decision-making execution process risks. After the validation of the model, a model simulation is conducted to predict the dynamic evolution process of the TDMRs. As a result, a multilayer risk list consisting of 42 index layer risk indicators, 13 field layer risk indicators, and 3 standard layer risk indicators is identified. The SD modeling results show that these multilevel TDMRs interact dynamically and have intricate influences on the total risk level of TDMs in megaprojects. The results of this study could be useful for decision-makers to identify and mitigate TDMRs in megaprojects.
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