Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145597
Title: Bayesian belief network-based project complexity measurement considering causal relationships
Authors: Luo, Lan
Zhang, Limao
Wu, Guangdong
Keywords: Engineering::Civil engineering
Issue Date: 2020
Source: Luo, L., Zhang, L., & Wu, G. (2020). Bayesian belief network-based project complexity measurement considering causal relationships. Journal of Civil Engineering and Management, 26(2), 200-215. doi:10.3846/jcem.2020.11930
Journal: Journal of Civil Engineering and Management
Abstract: This research proposes a Bayesian belief network-based approach to measure the project complexity in the construction industry. Firstly, project complexity nodes are identified for model development based on the literature review. Secondly, the project complexity measurement model is developed with 225 training samples and validated with 20 test samples. Thirdly, the developed measurement model is utilized to conduct model analytics for sequential decision making, which includes predictive, diagnostic, sensitivity, and influence chain analysis. Finally, EXPO 2010 is used to testify the effectiveness and applicability of the proposed approach. Results indicate that (1) more attention should be paid on technological complexity, information complexity, and task complexity in the process of complexity management; (2) the proposed measurement model can be applied into practice to predict the complexity level for a specific project. The uniqueness of this study lies in developing project complexity measurement model (PCMM) with the cause-effect relationships taken into account. This research contributes to (a) the state of knowledge by proposing a method that is capable of measuring the complexity level under what-if scenarios for complexity management, and (b) the state of practice by providing insights into a better understanding of causal relationships among influencing factors of complexity in construction projects.
URI: https://hdl.handle.net/10356/145597
ISSN: 1392-3730
DOI: 10.3846/jcem.2020.11930
Rights: © 2020 The Author(s). Published by VGTU Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unre-stricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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