• IDOWU AYOADE Department of Mechatronics Engineering, The First Technical University Ibadan, Ibadan, Nigeria.
  • ADEYINKA ADEDIGBA Department of Mechatronics Engineering, Federal University of Technology, Minna, Nigeria.
  • OLUSEYI AFOLABI ADEYEMI School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom.
  • OYETUNDE ADEOYE ADEAGA Department of Mechatronics Engineering, The First Technical University Ibadan, Ibadan, Nigeria.
  • OREOLUWA AKANBI Department of Mechatronics Engineering, The First Technical University Ibadan, Ibadan, Nigeria.


digital twin, smart infrastructures, digital asset management, literature review


The plethora of technologically developed software and digital types of machinery are widely applied for industrial production and the digitalization of building technologies. The fourth industrial revolution and the underlying digital transformation, known as Industry 4.0 is reshaping the way individuals live and work fundamentally. However, the advent of Industry 5.0 remodels the representation of industrial data for digitalization. As a result, massive data of different types are being produced. However, these data are hysteretic and isolated from each other, leading to low efficiency and low utilization of these valuable data. Simulation based on the theoretical and static model has been a conventional and powerful tool for the verification, validation, and optimization of a system in its early planning stage, but no attention is paid to the simulation application during system run-time. Dynamic simulation of various systems and the digitalization of the same is made possible using the framework available with Digital Twin. After a complete search of several databases and careful selection according to the proposed criteria, 63 academic publications about digital twin are identified and classified. This paper conducts a comprehensive and in-depth review of this literature to analyze the digital twin from the perspective of concepts, technologies, and industrial applications.


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How to Cite

AYOADE, I., ADEDIGBA, A., ADEYEMI, O. A., ADEAGA, O. A., & AKANBI, O. (2022). TRENDS AND PROSPECTS OF DIGITAL TWIN TECHNOLOGIES: A REVIEW. Quantum Journal of Engineering, Science and Technology, 3(4), 11–24. Retrieved from