  This paper proposes an uncertain data clustering approach to quantitatively
analyze the complexity of prefabricated construction components through the
integration of quality performance-based measures with associated engineering
design information. The proposed model is constructed in three steps, which (1)
measure prefabricated construction product complexity (hereafter referred to as
product complexity) by introducing a Bayesian-based nonconforming quality
performance indicator; (2) score each type of product complexity by developing
a Hellinger distance-based distribution similarity measurement; and (3) cluster
products into homogeneous complexity groups by using the agglomerative
hierarchical clustering technique. An illustrative example is provided to
demonstrate the proposed approach, and a case study of an industrial company in
Edmonton, Canada, is conducted to validate the feasibility and applicability of
the proposed model. This research inventively defines and investigates product
complexity from the perspective of product quality performance with design
information associated. The research outcomes provide simplified,
interpretable, and informative insights for practitioners to better analyze and
manage product complexity. In addition to this practical contribution, a novel
hierarchical clustering technique is devised. This technique is capable of
clustering uncertain data (i.e., beta distributions) with lower computational
complexity and has the potential to be generalized to cluster all types of
uncertain data.
