Assessment of seismic vulnerability of urbaninfrastructure is an actual problem, since the damage caused byearthquakes is quite significant. Despite the complexity of suchtasks, today’s machine learning methods allow the use of “fast”methods for assessing seismic vulnerability. The article proposesa methodology for assessing the characteristics of typical urbanobjects that affect their seismic resistance; using classification andclustering methods. For the analysis, we use kmeans and hkmeansclustering methods, where the Euclidean distance is used as ameasure of proximity. The optimal number of clusters isdetermined using the Elbow method. A decision-making model onthe seismic resistance of an urban object is presented, also themost important variables that have the greatest impact on theseismic resistance of an urban object are identified. The studyshows that the results of clustering coincide with expert estimates,and the characteristic of typical urban objects can be determinedas a result of data modeling using clustering algorithms
