Abstract
Geotechnical site clustering refers to identifying (or learning) groups in a site-labelled soil/rock database based on inter-site similarity. It is common to cluster sites in a “region” in practice, be it a geographical area or a geologic zone. Data-driven clustering may produce solutions outside these conventional geographical/geologic demarcations. Hence, it is termed “quasi-regional” clustering. This study presents a spectral algorithm for quasi-regional clustering based on a recently proposed hierarchical Bayesian site similarity measure (HBSSM). The HBSSM is utilized to construct the inter-site similarity matrix of the site-labelled database. Quasi-regional clustering is thereafter performed based on the eigenvectors of a transformed similarity matrix. Using numerical and real examples, it is shown that the presented clustering algorithm can produce reasonable clustering results. The proposed clustering algorithm is applicable to geotechnical sites with Multivariate, Uncertain and Unique, Sparse and InComplete (MUSIC) data. It only considers similarity in means, variances, and cross-correlations. The similarity in the spatial variation (e.g., with respect to depth) is not considered.