Real-time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Abstract

Tunnels are essential components of contemporary infrastructure, yet guaranteeing their safety, longevity, and efficiency remains a persistent challenge. Recent breakthroughs in artificial intelligence (AI) and digital twin (DT) technology provide innovative solutions for the real-time monitoring of tunnel systems, suggesting proactive maintenance tactics and improved safety protocols. This review paper offers a comprehensive examination of the application of AI and DT methodologies in tunnel surveillance. We explore the core concepts of AI and DT and their applicability to structural monitoring, encompassing machine learning, computer vision, and sensor integration. Through the utilization of these AI-powered technologies, engineers are equipped with unparalleled insights into the state and behavior of tunnels, facilitating the early identification of irregularities and the optimization of maintenance timelines. We discuss the array of AI techniques utilized for the immediate monitoring of tunnel systems, emphasizing their foundations, benefits, and practical uses. Numerous studies have showcased the effectiveness and adaptability of AI-based monitoring systems in various tunnel settings. Moreover, we address the hurdles and constraints inherent in AI and DT methodologies and suggest strategies for overcoming them, such as data augmentation, interpretable AI, edge computing, and continuous monitoring. Ultimately, the incorporation of AI and DT technologies into tunnel surveillance signifies a paradigm shift, offering substantial advantages over conventional techniques. By adopting AI-driven monitoring systems, tunnel operators can augment safety, prolong the lifespan of infrastructure, and decrease operational expenses, molding the future of subterranean infrastructure management.

Keywords

artificial intelligence / digital twin / machine learning / monitoring / real-time / tunnelling

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