Machine learning application to disaster damage repair cost modelling of residential buildings

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dc.contributor.authorWanigarathna, Nadeeshani
dc.contributor.authorXie, Ying
dc.contributor.authorHenjewele, Christian
dc.contributor.authorMorga, Mariantonietta
dc.contributor.authorJones, Keith
dc.date.accessioned2025-01-09T12:17:45Z
dc.date.available2025-01-09T12:17:45Z
dc.date.freetoread2025-01-09
dc.date.issued2025
dc.date.pubOnline2024-12-08
dc.descriptionData used for this research is publicly available in the following portal. https://openricostruzione.regione.emilia-romagna.it/ricostruzione-privata
dc.description.abstractRestoring residential buildings following earthquake damage requires a significant level of resources. Being able to predict these resource requirements in advance and accurately improves the effectiveness of disaster preparedness and subsequent recovery activities. This research explored how the latest ML algorithms could be used for antecedent earthquake loss modelling. A cost database for repairing residential buildings damaged by the Emilia Romagna earthquake in Italy was analysed using six state-of-the-art ML models to explore their ability to predict repair cost rates(cost per floor area) for a domestic building damaged by earthquakes. A Gradient Boost Regression model outperformed five other models in predicting earthquake damage repair cost rate. The performance of this model was significantly accurate and covers about 76% of the cases. A further SHAP analysis revealed that operational level, damage level and non-housing area of the buildings as top 3 important features when predicting the resultant damage repair cost rate. Overall this research advanced antecedent earthquake loss modelling approaches to increase the accuracy of estimates by incorporating more variables than the widely used damage level based simple methodology.
dc.description.journalNameConstruction Management and Economics
dc.description.sponsorshipEuropean Commission
dc.description.sponsorshipThis work was supported by the European Union’s Horizon 2020 under Grant No 700748
dc.format.extentpp. 1-21
dc.identifier.citationWanigarathna N, Xie Y, Henjewele C, et al., (2024) Machine learning application to disaster damage repair cost modelling of residential buildings. Construction Management and Economics, Available online 8 December 2024
dc.identifier.eissn1466-433X
dc.identifier.elementsID561019
dc.identifier.issn0144-6193
dc.identifier.issueNoahead-of-print
dc.identifier.urihttps://doi.org/10.1080/01446193.2024.2419413
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23328
dc.identifier.volumeNoahead-of-print
dc.languageEnglish
dc.language.isoen
dc.publisherTaylor and Francis
dc.publisher.urihttps://www.tandfonline.com/doi/full/10.1080/01446193.2024.2419413
dc.relation.isreferencedbyhttps://openricostruzione.regione.emilia-romagna.it/ricostruzione-privata
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEarthquake
dc.subjectcost modelling
dc.subjectmachine learning
dc.subjectdamage repair costs
dc.subjectdisaster preparedness
dc.subject4005 Civil Engineering
dc.subject40 Engineering
dc.subject33 Built Environment and Design
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectBuilding & Construction
dc.subject33 Built environment and design
dc.subject38 Economics
dc.subject40 Engineering
dc.titleMachine learning application to disaster damage repair cost modelling of residential buildings
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-10-16

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