Development of data-mining technique for seismic vulnerability assessment
Wojcik W. Karmenova M. Smailova S. Tlebaldinova A. Belbeubaev A.
2021Polska Akademia Nauk
International Journal of Electronics and Telecommunications
2021#67Issue 2261 - 266 pp.
Assessment of seismic vulnerability of urban infrastructure is an actual problem, since the damage caused by earthquakes is quite significant. Despite the complexity of such tasks, todays machine learning methods allow the use of fast methods for assessing seismic vulnerability. The article proposes a methodology for assessing the characteristics of typical urban objects that affect their seismic resistance; using classification and clustering methods. For the analysis, we use kmeans and hkmeans clustering methods, where the Euclidean distance is used as a measure of proximity. The optimal number of clusters is determined using the Elbow method. A decision-making model on the seismic resistance of an urban object is presented, also the most important variables that have the greatest impact on the seismic resistance of an urban object are identified. The study shows that the results of clustering coincide with expert estimates, and the characteristic of typical urban objects can be determined as a result of data modeling using clustering algorithms.
Clustering , Data analysis , Hkmeans , Random forest , Seismic assessment
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Lublin Technical University, Poland
D. Serikbayev East Kazakhstan State Technical University, Kazakhstan
S. Amanzholov East Kazakhstan State University, Kazakhstan
Cukurova University, Turkey
Lublin Technical University
D. Serikbayev East Kazakhstan State Technical University
S. Amanzholov East Kazakhstan State University
Cukurova University
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