Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162677
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dc.contributor.authorLuo, Lanen_US
dc.contributor.authorZhang, Limaoen_US
dc.contributor.authorYang, Deleien_US
dc.contributor.authorHe, Qinghuaen_US
dc.date.accessioned2022-11-03T08:19:29Z-
dc.date.available2022-11-03T08:19:29Z-
dc.date.issued2022-
dc.identifier.citationLuo, L., Zhang, L., Yang, D. & He, Q. (2022). A probabilistic approach to assessing project complexity dynamics under uncertainty. Soft Computing, 26(8), 3969-3985. https://dx.doi.org/10.1007/s00500-021-06491-wen_US
dc.identifier.issn1432-7643en_US
dc.identifier.urihttps://hdl.handle.net/10356/162677-
dc.description.abstractIntractable complexity may be encountered as the construction project advances. Existing research rarely investigates the time-updated dynamic in project complexity as the project process progresses. This study develops a novel systematic soft computing approach based on Bayesian inference to explore the evolutionary dynamics in project complexity under uncertainty. By learning the network structure and parameters from given data, a dynamic Bayesian network model is established to simulate the complex interrelations among 7 complexity-related variables. The developed approach is capable of performing predictive, sensitivity, and diagnostic analysis on a quantitative basis. The construction project of EXPO 2010 is used to testify the effectiveness and applicability of the developed approach. Results indicate that (1) more attention should be paid to technological complexity and task complexity in the process of complexity management; (2) the developed dynamic Bayesian network approach can model the evolutionary dynamics of project complexity at different scenarios; and (3) the complexity level of a specific construction project over time can be predicted in a dynamic manner. This research contributes to (a) the state of the knowledge by proposing a systematic soft computing methodology that can model and identify the dynamic interactions of project complexity factors over time, and (b) the state of the practice by gaining a better understanding of the most sensitive factors for managing complexity in a changing project environment.en_US
dc.language.isoenen_US
dc.relation.ispartofSoft Computingen_US
dc.rights© 2021 The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titleA probabilistic approach to assessing project complexity dynamics under uncertaintyen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doi10.1007/s00500-021-06491-w-
dc.identifier.scopus2-s2.0-85119300914-
dc.identifier.issue8en_US
dc.identifier.volume26en_US
dc.identifier.spage3969en_US
dc.identifier.epage3985en_US
dc.subject.keywordsEvolutionary Dynamicsen_US
dc.subject.keywordsBayesian Networken_US
dc.description.acknowledgementThis study is supported by the National Natural Science Foundation of China (72061025, 71901113, 71640012, 71801083, and 71971161), Social Science Foundation of Jiangxi Province (21GL05), and China Scholarship Council Project (201806825066)en_US
item.grantfulltextnone-
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