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    Dynamic collective opinion generation framework for digital transformation barrier analysis in the construction industry
    (Elsevier, 2024) Chen, Zhen-Song; Liang, Chong-Ze; Xu, Ya-Qiang; Pedrycz, Witold; Skibniewski, Miroslaw J.
    The absence of a reliable, dynamic evaluation system has impeded early-stage industrial research progress, particularly in the digital transformation of the construction industry. Moreover, existing research studies rarely explore the impact of digitalt transformation barriers considering the interplays among them. This paper aims to introduce an innovative framework to generate dynamic collective opinions for barrier analysis in such context. The proposed dynamic collective opinion generation framework comprises three key components: Collective Opinion Generation, Prediction with Expert Advice (PEA), and Social Network Analysis. Its goal is to provide dependable decision support when subjective evaluation data from experts is available. Initially, a bi-objective optimization model generates the initial barrier weight vector. The PEA incorporates a loss function to measure the deviation between aggregated probablity density function and actual observed data, updating the weight vector over time. Next, an influence network covering all barriers is established. Node significance is evaluated through metrics like degree centrality, closeness centrality, and eigenvector centrality. The gravity model based on three metrics is used to determine interrelationships among barriers, resulting in a weight vector capturing these interplays. The two weight vectors are combined with Nash equilibrium, yielding the ultimate weight vector for barriers. The effectiveness of the proposed dynamic collective opinion generation framework is showcased through a case study on China Construction Third Bureau. Results indicate that talent structure notably influences construction companies' digital transformation. Additionally, market structure and strategic position significantly impact digital transformation in this industry.
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    Multiobjective optimization-based decision support for building digital twin maturity measurement
    (Elsevier Sci Ltd, 2024) Chen, Zhen-Song; Chen, Kou-Dan; Xu, Ya-Qiang; Pedrycz, Witold; Skibniewski, Miroslaw J.
    The digital twin (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 multiobjective optimization 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.