Seismic Assessment of Urban Buildings Using Data Mining Methods

Abstract

Seismic assessment of an urban environment is a time-consuming and complicated task that also requires considerable financial resources. In this paper, we suggest an approach to the seismic evaluation of urban buildings based on data mining methods. Regarding this topic, researches have been conducted on 163 typical objects with 19 different features including various types of buildings. The k-means method was used to create clusters of similar objects, it also allowed us to determine dependencies between data. More accurate clustering models were obtained using a hierarchical algorithm. The quality of the proposed methods was evaluated to ensure the reliability of the obtained results.