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.
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