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Assessing collusion risks in managing construction projects using artificial neural network

    Ming Shan Affiliation
    ; Yun Le Affiliation
    ; Kenneth T. W. Yiu Affiliation
    ; Albert P. C. Chan Affiliation
    ; Yi Hu Affiliation
    ; You Zhou Affiliation

Abstract

Being an insidious risk to construction projects, collusion has attracted extensive attention from numerous researchers around the world. However, little effort has ever been made to assess collusion, which is important and necessary for curbing collusion in construction projects. Specific to the context of China, this paper developed an artificial neural network model to assess collusion risk in construction projects. Based on a comprehensive literature review, a total of 22 specific collusive practices were identified first, and then refined by a two-round Delphi interview with 15 experienced experts. Subsequently, using the consolidated framework of collusive practices, a questionnaire was further developed and disseminated, which received 97 valid replies. The questionnaire data were then utilized to develop and validate the collusion risk assessment model with the facilitation of artificial neural network approach. The developed model was finally applied in a real-life metro project in which its reliability and applicability were both verified. Although the model was developed under the context of Chinese construction projects, its developing strategy can be applied in other countries, especially for those emerging economies that have a significant concern of collusion in their construction sectors, and thus contributing to the global body of knowledge of collusion.

Keyword : collusion risk, construction project, artificial neural network, China

How to Cite
Shan, M., Le, Y., Yiu, K. T. W., Chan, A. P. C., Hu, Y., & Zhou, Y. (2018). Assessing collusion risks in managing construction projects using artificial neural network. Technological and Economic Development of Economy, 24(5), 2003-2025. https://doi.org/10.3846/20294913.2017.1303648
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Oct 12, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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