Systematic Mapping of Global Research on Disaster Damage Estimation for Buildings: A Machine Learning-Aided Study

Simple item page
dc.citation.issue6
dc.citation.volume14
dc.contributor.authorRajapaksha D
dc.contributor.authorSiriwardana C
dc.contributor.authorRuparathna R
dc.contributor.authorMaqsood T
dc.contributor.authorSetunge S
dc.contributor.authorRajapakse L
dc.contributor.authorDe Silva S
dc.contributor.editorWitt E
dc.contributor.editorBilau AA
dc.contributor.editorSun B
dc.date.accessioned2024-08-15T21:22:35Z
dc.date.available2024-08-15T21:22:35Z
dc.date.issued2024-06-20
dc.description.abstractResearch on disaster damage estimation for buildings has gained extensive attention due to the increased number of disastrous events, facilitating risk assessment, the effective integration of disaster resilience measures, and policy development. A systematic mapping study has been conducted, focusing on disaster damage estimation studies to identify trends, relationships, and gaps in this large and exponentially growing subject area. A novel approach using machine learning algorithms to screen, categorise, and map the articles was adopted to mitigate the constraints of manual handling. Out of 8608 articles from major scientific databases, the most relevant 2186 were used in the analysis. These articles were classified based on the hazard, geographical location, damage function properties, and building properties. Key observations reveal an emerging trend in publications, with most studies concentrated in developed and severely disaster-affected countries in America, Europe, and Asia. A significant portion (68%) of the relevant articles focus on earthquakes. However, as the key research opportunities, a notable research gap exists in studies focusing on the African and South American continents despite the significant damage caused by disasters there. Additionally, studies on floods, hurricanes, and tsunamis are minimal compared to those on earthquakes. Further trends and relationships in current studies were analysed to convey insights from the literature, identifying research gaps in terms of hazards, geographical locations, and other relevant parameters. These insights aim to effectively guide future research in disaster damage estimation for buildings.
dc.description.confidentialfalse
dc.edition.editionJune 2024
dc.identifier.citationRajapaksha D, Siriwardana C, Ruparathna R, Maqsood T, Setunge S, Rajapakse L, De Silva S. (2024). Systematic Mapping of Global Research on Disaster Damage Estimation for Buildings: A Machine Learning-Aided Study. Buildings. 14. 6.
dc.identifier.doi10.3390/buildings14061864
dc.identifier.eissn2075-5309
dc.identifier.elements-typejournal-article
dc.identifier.number1864
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71319
dc.languageEnglish
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/2075-5309/14/6/1864
dc.relation.isPartOfBuildings
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdamage estimation
dc.subjectfragility curves
dc.subjectvulnerability curves
dc.subjectnatural hazards
dc.subjectbuildings
dc.subjectmachine learning
dc.titleSystematic Mapping of Global Research on Disaster Damage Estimation for Buildings: A Machine Learning-Aided Study
dc.typeJournal article
pubs.elements-id489652
pubs.organisational-groupCollege of Health
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Published version.pdf
Size:
4.94 MB
Format:
Adobe Portable Document Format
Description:
489652 PDF.pdf
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
9.22 KB
Format:
Plain Text
Description:
Download
Collections
Journal Articles