The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Classifying earthquake damage to buildings using machine learning
Abstract
The ability to rapidly assess the spatial distribution and severity of building damage is essential to post‐event emergency response and recovery. Visually identifying and classifying individual building damage requires significant time and personnel resources and can last for months after the event. This article evaluates the feasibility of using machine learning techniques such as discriminant analysis, k ‐nearest neighbors, decision trees, and random forests, to rapidly predict earthquake‐induced building damage. Data from the 2014 South Napa earthquake are used for the study where building damage is classified based on the assigned Applied Technology Council (ATC)‐20 tag (red, yellow, and green). Spectral acceleration at a period of 0.3 s, fault distance, and several building specific characteristics (e.g. age, floor area, presence of plan irregularity) are used as features or predictor variables for the machine learning models. A portion of the damage data from the Napa earthquake is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. It is noted that the random forest algorithm can accurately predict the assigned tags for 66% of the buildings in the test dataset.
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Cites
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Cited by
Sun, H., Burton, H. V., & Huang, H. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101816 · Cited by 544
Self-citationGuan, X., & Burton, H. (2022) · Structures · doi:10.1016/j.istruc.2022.10.004 · Cited by 47
Self-citationChen, J., Tang, H., Ge, J., & Pan, Y. (2022) · Remote Sensing · doi:10.3390/rs14061358 · Cited by 31
Metadata
- Authors
- Mangalathu, S., Sun, H., Nweke, C. C., Yi, Z., & Burton, H. V.
- Venue
- Earthquake Spectra
- Publisher
- Wiley
- Published
- 2020-02
- Volume
- 36
- Issue
- 1
- Pages
- 183-208
- ISSN
- 1944-8201 (ISSN-L)
- 8755-2930
- OpenAlex
- W2974551902
- Cited by
- 298
- References
- 13
- FWCI
- 18.31
- Type
- journal article
- Language
- en
- Open access
- closed
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