LAUSR

This article presents a framework to cluster buildings into typologically similar groups and select indicator buildings for regional seismic response and damage analysis. The framework requires a robust database of buildings to provide high-level structural and site information of buildings. Here, a database of 234 reinforced concrete buildings with five or more above-ground stories in the central business district of Wellington, New Zealand, has been selected as the case study of this research. First, key structural and site parameters that contribute to the seismic demand, response, and damage of each building are extracted from the database. Extracted parameters comprise three numerical and five categorical attributes of each building, including the year of construction, height, period, lateral load resisting system, floor system, site subsoil class, importance level, and strong motion station. Next, two prominent unsupervised machine learning clustering approaches are utilized to cluster the mixed categorical and numerical building database: k-prototype on the mixed numerical and categorical database and k-means on principal components numerical subspace adopted from factor analysis of mixed data (FAMD). A novel autoencoder deep learning neural network is also designed and trained to convert the mixed data into a low-dimensional subspace called latent space and feed this into k-means for clustering. The proposed autoencoder method is demonstrated to be more effective at clustering buildings into useful typological clusters for seismic response and damage analysis based on multiple criteria from both data-science and engineering perspectives. The details of selected indicator buildings for each similar seismic vulnerability cluster are also represented.