02064nas a2200157   4500000000100000008004100001260001200042100001900054700001700073700001600090700001900106700002600125245010200151490000700253520164600260       2024                            d  c01/20241 aZhen-Song Chen1 aKou-Dan Chen1 aYa-Qiang Xu1 aWitold Pedrycz1 aMirosław Skibniewski00aMultiobjective optimization-based decision support for building digital twin maturity measurement0 v593 a<p>The&nbsp;<a href="https://www.sciencedirect.com/topics/computer-science/digital-twin" title="Learn more about digital twin from ScienceDirect's AI-generated Topic Pages">digital twin</a>&nbsp;(DT) represents a powerful tool for advancing construction industry to provide a cyber–physical integration that enables real-time monitoring of assets and activities and facilitates decision-making. Due to the inherent characteristics of the construction industry and the diverse possibilities with DT, proliferation of building digital twin (BDT) necessitates a comprehensive comprehension of its evolution and the creation of roadmaps. This paper aims to contribute to the formalization and standardization of BDT. It designs a novel assessment framework for the overall maturity measurement of existing BDT projects. The developed BDT maturity model incorporates a collective opinion generation paradigm based on a fairness-aware&nbsp;<a href="https://www.sciencedirect.com/topics/engineering/multiobjective-optimization" title="Learn more about multiobjective optimization from ScienceDirect's AI-generated Topic Pages">multiobjective optimization</a>&nbsp;model to provide an expert-based evaluation system for evaluating the maturity of BDT projects. The effectiveness and feasibility of the proposed framework have been validated through a case study of an experimental BDT initiative. This paper establishes a generalizable framework for BDT maturity assessment that can offer insights into BDT maturity standards to construction practitioners to create effective strategies for the diffusion, development, and maturation of BDT.</p>