用数据和新兴技术塑造隧道技术的未来

Dayu Apoji, Brian Sheil, Kenichi Soga
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摘要

摘要 全球人口的增长和城市化给社会带来了巨大挑战:空间日益稀缺,日益恶化的基础设施供不应求,交通拥堵不堪,环境影响日益加剧。地下空间,尤其是隧道,在应对这些挑战方面可以发挥关键作用。然而,隧道工程的成本、风险、不确定性和复杂性阻碍了其发展。在本文中,我们设想了几种可能创新和改变机械化隧道行业的技术进步,包括人工智能(AI)、自主系统和生物启发系统。人工智能的普及可帮助人类工程师和操作员在隧道挖掘过程中根据大量实时数据做出系统性和定量化的明智决策。自主隧道掘进系统可实现精确和可预测的隧道掘进作业,只需极少的人工干预,并可促进大规模地下基础设施项目的建设,而这些项目以前使用传统方法是具有挑战性或不可行的。生物启发系统可为更高效的隧道设计和施工理念提供有价值的参考和策略。虽然这些技术进步可以带来巨大的希望,但它们也面临着相当大的挑战,例如提高隧道挖掘数据的可访问性和可共享性,开发稳健、可靠和可解释的机器学习系统,以及扩大生物启发系统的力学规模并确保其从原型水平到实际应用的适用性。要确保这些创新在未来隧道工程中的成功实施,应对这些挑战势在必行。
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Shaping the future of tunneling with data and emerging technologies
Abstract The increase in global population and urbanization is presenting significant challenges to society: space is becoming increasingly scarce, demand is exceeding capacity for deteriorating infrastructure, transportation is fraught with congestion, and environmental impacts are accelerating. Underground space, and particularly tunnels, has a key role to play in tackling these challenges. However, the cost, risk, uncertainty, and complexity of the tunneling process have impeded its growth. In this paper, we envision several technological advancements that can potentially innovate and transform the mechanized tunneling industry, including artificial intelligence (AI), autonomous, and bio-inspired systems. The proliferation of AI may assist human engineers and operators in making informed decisions systematically and quantitatively based on massive real-time data during tunneling. Autonomous tunneling systems may enable precise and predictable tunneling operations with minimal human intervention and facilitate the construction of massive and large-scale underground infrastructure projects that were previously challenging or unfeasible using conventional methods. Bio-inspired systems may provide valuable references and strategies for more efficient tunneling design and construction concepts. While these technological advancements can offer great promise, they also face considerable challenges, such as improving accessibility to and shareability of tunneling data, developing robust, reliable, and explainable machine learning systems, as well as scaling the mechanics and ensuring the applicability of bio-inspired systems from the prototype level to real-world applications. Addressing these challenges is imperative to ensure the successful implementation of these innovations for future tunneling.
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