Digital Twin for Underground Stations : Improving Decision Making for Construction Lifecycle

IF 0.3 Q4 ENGINEERING, GEOLOGICAL
Mengqi Huang, J. Ninić, Qianbing Zhang
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引用次数: 1

Abstract

Challenges in the extraction and use of earth resources and spaces are encountered given a growing worldwide population, rising infrastructures development, and widespread climate change. In Australia, mining and construction are two major bases for economic growth while both being traditional hazardous and heavy industries. A nation-wide infrastructure upgrade featuring large-scale underground development is underway, the geological uncertainties and localisation difficulties of already laid infrastructure are associated with challenges not seen in building construction. A safer and competent subterranean transport solution is yet proposed in the context of sustainable developments. In light of this, geotechnical analysis as a fundamental subject for developing and maintaining safe and sustainable use of underground space has huge potential to be undertaken more intuitively considering the advancements in information management and visualisation. The PhD work examines the state-of-the-art applications, limitations and future opportunities of Building Information Modelling (BIM) and other computational techniques in the digitisation of tunnelling and underground construction. The visualisation and interoperability facilitated by data-driven processes are especially important to underground construction that engages interdisciplinary and multi-environment interaction.
地下车站的数字孪生:改善施工生命周期的决策
随着世界人口的增长、基础设施的发展和气候的广泛变化,地球资源和空间的开采和利用面临着挑战。在澳大利亚,矿业和建筑业是经济增长的两个主要基础,同时也是传统的危险和重工业。以大规模地下开发为特征的全国范围内的基础设施升级正在进行中,已经铺设的基础设施的地质不确定性和本地化困难与建筑建设中未见的挑战相关。在可持续发展的背景下,还没有提出一个更安全、更有效的地下运输解决方案。鉴于此,考虑到信息管理和可视化的进步,岩土分析作为开发和维持地下空间安全和可持续利用的基础学科,具有更直观地承担的巨大潜力。博士学位研究了建筑信息模型(BIM)和其他计算技术在隧道和地下建筑数字化中的最新应用、局限性和未来机会。数据驱动过程促进的可视化和互操作性对于涉及跨学科和多环境交互的地下建设尤为重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Australian Geomechanics Journal
Australian Geomechanics Journal ENGINEERING, GEOLOGICAL-
CiteScore
0.40
自引率
0.00%
发文量
1
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