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Damage prediction for regular reinforced concrete buildings using the decision tree algorithm
Blekinge Institute of Technology, School of Computing.
2014 (English)In: Computers & structures, ISSN 0045-7949, E-ISSN 1879-2243, Vol. 130, 46-56 p.Article in journal (Refereed) Published
Abstract [en]

To overcome the problem of outlier data in the regression analysis for numerical-based damage spectra, the C4.5 decision tree learning algorithm is used to predict damage in reinforced concrete buildings in future earthquake scenarios. Reinforced concrete buildings are modelled as single-degree-of-freedom systems and various time-history nonlinear analyses are performed to create a dataset of damage indices. Subsequently, two decision trees are trained using the qualitative interpretations of those indices. The first decision tree determines whether damage occurs in an RC building. Consequently, the second decision tree predicts the severity of damage as repairable, beyond repair, or collapse.

Place, publisher, year, edition, pages
Elsevier , 2014. Vol. 130, 46-56 p.
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-6707DOI: 10.1016/j.compstruc.2013.10.006ISI: 000327808400005Local ID: oai:bth.se:forskinfo18394AEEACAF01D1C1257CC3003381CFOAI: oai:DiVA.org:bth-6707DiVA: diva2:834239
Available from: 2014-04-23 Created: 2014-04-23 Last updated: 2017-05-26Bibliographically approved

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Rezaee, Shaliz
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CiteExportLink to record
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