BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives
BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives
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DOI：
10.1016/j.tust.2020.103677
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发表时间：
2021-02-01
影响因子：
6.9
通讯作者：
Zhang, Q. B.
中科院分区：
文献类型：
--
作者：
Huang, M. Q.;Ninic, J.;Zhang, Q. B.
建筑、工程和建筑（AEC）行业正在经历一场由蓬勃发展的数字化和自动化驱动的技术革命。信息技术和计算机科学研究领域的进展，如建筑信息建模（BIM），机器学习和计算机视觉，由于其有用的应用而引起越来越多的关注。与此同时，以人口驱动的地下开发加快，数字化转型成为战略要务。城市地下基础设施是宝贵的资产，因此需要有效的规划、建设和维护。虽然地下施工的过程和子系统具有更高的可见性和可靠性，但BIM，机器学习和计算机视觉在地下施工中的应用代表了与地面施工不同的机遇和挑战。因此，本文旨在介绍BIM、机器学习、计算机视觉及其相关技术在促进隧道施工和地下建筑数字化转型方面的最新发展和未来趋势。第1节介绍了采用这些技术的全球需求。第2节介绍了相关的术语、标准化和基本原理。第3节回顾了传统和机械化隧道施工中的BIM，并强调了将3D地质建模和地理信息系统（GIS）数据库与BIM集成的重要性。第4节探讨了机器学习和计算机视觉在地下施工不同阶段的关键应用。第5节讨论了利用这些新兴技术在整个地下项目生命周期中提升数字化，自动化和信息集成的现有研究的挑战和前景。第6节总结了目前的发展状况，确定的差距和未来的方向。
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.