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An analytic network process model for risk quantification of mega construction projects

Erol, H., Dikmen, I., Atasoy, G. and Birgonul, M. T. (2022) An analytic network process model for risk quantification of mega construction projects. Expert Systems with Applications, 191. 116215. ISSN 0957-4174

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To link to this item DOI: 10.1016/j.eswa.2021.116215

Abstract/Summary

Risk, complexity, and uncertainty are inherent components of megaprojects due to their unique features. However, existing project management practices lack a structured synthesis of these concepts, which leads to unrealistic risk assessments, ineffective management strategies, and poor project performance. In order to fill this gap, this research aims to develop a holistic risk quantification approach incorporating risk-related concepts. For this purpose, a conceptual risk assessment process developed for mega construction projects was operationalized with an Analytic Network Process (ANP) model. The weights of the risk sources in the ANP model were determined by the domain experts through a two-round Delphi study. With the purpose of improving the efficiency and reliability of the knowledge elicitation process, the Delphi study was supported by an interactive data collection tool capable of ANP calculations. The resulting model helped to prioritize the risk sources in mega construction projects. The validity of the findings was tested through the data of 11 mega construction projects. Validation studies revealed the potential of the ANP-based model in quantifying the project risks. Hence, the novel approach proposed in this study is expected to contribute both to the literature by unveiling the interactions between risk-related concepts and to the practitioners by assisting them in assessing the project risks more realistically. Although the risk quantification model has been developed for mega construction projects, it can also be implemented in other project-based industries with minor modifications.

Item Type:Article
Refereed:Yes
Divisions:Science > School of the Built Environment > Construction Management and Engineering
ID Code:105895
Publisher:Elsevier

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