### Development of data-mining technique for seismic vulnerability assessment

#### Abstract

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, today’s 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.

#### Full Text:

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