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The "regional advantage" hypothesizes that inference uncertainty/prediction error of geotechnical or geological properties at a target site (a site contains a group of records measured from different locations/depths using a variety of tests) can be smaller if we use a quasi-regional cluster (includes two or more database sites with geotechnical or geological properties similar to the target site) instead of the entire database. A tailored clustering enabled regionalization (TCER) framework has been proposed to verify this "regional advantage" hypothesis. TCER requires the target site should not be an outlier site relative to the database. However, it remains a challenge on how to detect an outlier site (or data group) from a database. In this paper, we modify the original TCER by introducing a novel outlier site detection step called maximum site similarity (MSS) into the original TCER. The capability of MSS is verified using synthetic and real examples. Additionally, three inference methods [e.g., probabilistic multiple regression (PMR), classical Bayesian model (CBM), and hierarchical Bayesian model (HBM)] are studied for the purpose of determining the optimal inference method for the modified TCER in terms of achieving the minimum inference uncertainty/prediction error with reasonable computational time. It is shown that the modified TCER with CBM outperforms other inference methods for the examples shown in this paper. • An efficient method that addresses a non-traditional class of outlier site detection problems is proposed. • The proposed outlier detection method is added to the original TCER framework for screening appropriate database. • A Shenzhen clay database named SZ-CLAY/7/3426 is compiled to validate the modified TCER framework. • Three inference methods are applied in the modified TCER framework and their inference uncertainties are compared. [ABSTRACT FROM AUTHOR]

Classical clustering (CC) normally divides a database into a predetermined number of fixed clusters before assigning each target site into one of these clusters to build a quasi-local model. Because the range of target sites is much larger than the range of clusters, it is possible that the identified cluster contains database sites that are weakly correlated to some assigned target sites, especially when these target sites are located near the cluster boundaries. The existence of weakly correlated sites in the cluster may make the quasi-local model less effective in predicting soil properties at the target site. This paper proposes a Tailored Clustering Enabled Regionalization (TCER) framework that can minimize the inference uncertainty at a target site. It mainly consists of two parts: (1) a site similarity measure to quantify the similarity between database sites and the target site and (2) a novel tailored clustering (TC) approach to identify the optimal cluster (called quasi-regional cluster) of database sites with the highest site similarity measures. In comparison to the traditional TC, the novel TC does not require users to specify a site similarity threshold that divides database sites into those within the cluster and those outside the cluster. Finally, a hierarchical Bayesian model is applied to the quasi-regional cluster (rather than the entire database) to infer geotechnical/geological properties at the target site. The capability of TCER is verified using synthetic and real examples. It is shown that the proposed TCER is computationally efficient and can reduce inference uncertainty. • An inter-dataset similarity measure is modified to make it suitable for comparing the similarity between two sites. • A novel clustering approach is proposed for identifying a cluster that is highly correlated to the target site. • A tailored clustering enabled regionalization framework is proposed for reducing inference uncertainty of a target site. [ABSTRACT FROM AUTHOR]