Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154426
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dc.contributor.authorChen, Chenen_US
dc.contributor.authorZhang, Limaoen_US
dc.contributor.authorTiong, Robert Lee Kongen_US
dc.date.accessioned2021-12-22T07:09:11Z-
dc.date.available2021-12-22T07:09:11Z-
dc.date.issued2020-
dc.identifier.citationChen, C., Zhang, L. & Tiong, R. L. K. (2020). A novel learning cloud Bayesian network for risk measurement. Applied Soft Computing Journal, 87, 105947-. https://dx.doi.org/10.1016/j.asoc.2019.105947en_US
dc.identifier.issn1568-4946en_US
dc.identifier.urihttps://hdl.handle.net/10356/154426-
dc.description.abstractBayesian network (BN) is a popularly used approach for risk analysis. Because it is a graphic model being able to deal with randomness yet unable to model ambiguity, the fuzzy set theory is often combined with it to create a so-called fuzzy BN. Instead of using the classical fuzzy set theory, this paper intends to combine a normal Cloud model with the BN. In the normal Cloud model, an element belonging to a certain qualitative concept is not certain and precise as well. The Cloud BN is a generalization of the fuzzy BN. It is more adaptive for the uncertainty description of linguistic concepts, for example, the risks. Using the normal Cloud model, the following numerical characteristics of the variables can be estimated: the expectation, the dispersion degree compared with the expectation, and the dispersion degree of entropy. Consequently, the risk assessment contains a richer set of analytical information. Cloud BNs attract growing research interests. Compared to its precedents, the Cloud BN in this paper has a learning capability. Since the risk factors may have a combined effect, the causal relationships among the variables can be very complex, and hidden variables may exist. The learning mechanism allows for automatic structure discovery from data, giving rise to a dynamically evolving network. The proposed learning Cloud BN is able to represent the real risk situation better than its precedents. Its effectiveness and applicability are demonstrated by an illustrative case for risk prediction of the face instability in an underground tunnel construction project.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relationM4082160.030en_US
dc.relationM4011971.030en_US
dc.relation.ispartofApplied Soft Computing Journalen_US
dc.rights© 2019 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titleA novel learning cloud Bayesian network for risk measurementen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doi10.1016/j.asoc.2019.105947-
dc.identifier.scopus2-s2.0-85076009216-
dc.identifier.volume87en_US
dc.identifier.spage105947en_US
dc.subject.keywordsBayesian Networken_US
dc.subject.keywordsUncertainty Modelingen_US
dc.description.acknowledgementThe Start-Up Grant at Nanyang Technological University, Singapore (No. M4082160.030) and the Ministry of Education Grant, Singapore (No. M4011971.030) are acknowledged for their financial support of this research.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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