BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives

Huang, M.Q.; Nini?, J.; Zhang, Q.B.

Abstract

The architecture, engineering and construction (AEC) industry is experiencing a technological revolution driven by booming digitisation and automation. Advances in research fields of information technology and computer science, such as building information modelling (BIM), machine learning and computer vision have attracted growing attention owing to their useful applications. At the same time, population-driven underground development has been accelerated with digital transformation as a strategic imperative. Urban underground infrastructures are valuable assets and thus demanding effective planning, construction and maintenance. While enabling greater visibility and reliability into the processes and subsystems of underground construction, applications of BIM, machine learning and computer vision in underground construction represent different sets of opportunities and challenges from their use in above-ground construction. Therefore, this paper aims to present the state-of-the-art development and future trends of BIM, machine learning, computer vision and their related technologies in facilitating the digital transition of tunnelling and underground construction. Section 1 presents the global demand for adopting these technologies. Section 2 introduces the related terminologies, standardisations and fundamentals. Section 3 reviews BIM in traditional and mechanised tunnelling and highlights the importance of integrating 3D geological modelling and geographic information system (GIS) databases with BIM. Section 4 examines the key applications of machine learning and computer vision at different stages of underground construction. Section 5 discusses the challenges and perspectives of existing research on leveraging these emerging technologies for escalating digitisation, automation and information integration throughout underground project lifecycle. Section 6 summarises the current state of development, identified gaps and future directions.

Citation

Huang, M., Nini?, J., & Zhang, Q. (2021). BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives. Tunnelling and Underground Space Technology, 108, Article 103677. https://doi.org/10.1016/j.tust.2020.103677

• Journal Article Type: Article
• Acceptance Date: Oct 10, 2020
• Online Publication Date: Dec 13, 2020
• Publication Date: 2021-02
• Deposit Date: Feb 26, 2021
• Publicly Available Date: Dec 14, 2022
• Journal: Tunnelling and Underground Space Technology
• Print ISSN: 0886-7798
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 108
• Article Number: 103677
• DOI: https://doi.org/10.1016/j.tust.2020.103677
• Keywords: Geotechnical Engineering and Engineering Geology; Building and Construction
• Public URL: https://nottingham-repository.worktribe.com/output/5353015
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0886779820306313
• Additional Information: This article is maintained by: Elsevier; Article Title: BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives; Journal Title: Tunnelling and Underground Space Technology; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.tust.2020.103677; Content Type: article; Copyright: © 2020 Elsevier Ltd. All rights reserved.