An Evaluation of Capabilities, Benefits, and Challenges of Developing Digital Twin Models for Sustainable Development

Abstract

Since the recent AECO industry has increasingly focused on sustainable development, with an emphasis on achieving long-term goals like enhancing eco-sustainability and durability, the demand for applying digital and revolutionary technologies has increased. Digital twin technology, enabling a digital model to represent a physical entity in real-time dynamically, has gained wide attention in manufacturing, aerospace, and healthcare. Although digital twin technology, which integrates with various digital technical tools, has been explored by some researchers, The overall understanding of how digital twin technology can be applied to sustainable construction is still unclear. This knowledge gap leads to unnecessary difficulty and hinders the full realization of digital twin capacities in sustainable development. This research conducts a literature review to examine the current state of digital twins and related technologies in the AECO industry, aiming to bridge this gap. A variety of technologies, tools, and algorithms employed in the applications of digital twin technology have been analyzed. The results present the four major processes of establishing digital twin models for sustainable development: data harvesting, data transmission and processing, modeling and simulation, and decision-making process. Additionally, four distinct scenarios within the decision-making process relevant to sustainable construction are specified. Furthermore, the digital twins' capabilities, benefits, and challenges have been evaluated. Although digital twin models cooperating with extensive technologies have capabilities and benefits in terms of modeling and visualization, real-time simulation and monitoring, data integration and analysis, and making predictive decisions in optimization, challenges still exist and need to be addressed in future applications. This review highlights the challenges of digital twin technology, including data security, data integration, and interoperability, which provides future research directions for digital twin studies.

Keywords: Digital Twin Technologies, Sustainable Development, Data Harvesting, Data Processing, Simulation, Decision-making

DOI: 10.54941/ahfe1005732