Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering

TL;DR

This paper introduces a novel approach combining Bayesian quality indicators and hierarchical clustering to analyze and categorize the complexity of prefabricated construction products using uncertain data.

Contribution

It presents a new method integrating quality performance measures with design info and a hierarchical clustering technique for uncertain data, specifically applied to prefabricated construction products.

Findings

Effective clustering of product complexities demonstrated in case study.

The approach provides interpretable insights for managing product complexity.

The hierarchical clustering method reduces computational complexity for uncertain data.

Abstract

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.