Automation of construction machines has grown rapidly in recent years as a response to the need to increase productivity, increase construction safety, decrease costs, and overcome the lack of availability of qualified labor. However, tunnel automation still lags. Automation of mechanized tunneling is essential to the future of tunneling construction, as the current practice still relies heavily on the experience and the human judgment of the machine operator to steer the TBM, which could lead to undesirable events. The primary motivation for this review paper stems from the statement: the success of tunneling automation relies on precise ground prediction. Even small inaccuracies can have significant implications, and the current reliance on human experience and judgment presents limitations and risks. With the abundance of machine data now available from TBMs, and the advancements in data analytics and machine learning (ML), many models have been proposed. These models have the potential to revolutionize tunneling by providing better decision-making support, including geology forecasts and anomaly detection. However, despite the numerous research studies and advantages of these models, they are not widely implemented in real-world scenarios. Thus, this review paper aims to address this issue by focusing on ground prediction models for TBM tunnels, providing a comprehensive overview of the current state-of-the-art, illuminating the existing practices, and highlighting the limitations that hinder these models from being the catalysts of tunneling automation. By emphasizing these challenges, the paper seeks to not just critique but also guide, providing recommendations for future research that promise to bridge the gaps and potentially usher in an era of fully automated TBMs.

中文翻译：

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迈向机械化隧道掘进自动化“探索大数据分析在 TBM 隧道地面预测中的应用”

近年来，为了满足提高生产率、提高施工安全性、降低成本和克服合格劳动力缺乏的需求，建筑机械自动化迅速发展。然而，隧道自动化仍然滞后。机械化隧道掘进的自动化对于隧道施工的未来至关重要，因为目前的实践仍然严重依赖机器操作员的经验和人为判断来操纵TBM，这可能会导致不良事件。这篇评论论文的主要动机源于这样的陈述：隧道自动化的成功依赖于精确的地面预测。即使很小的误差也会产生重大影响，而目前对人类经验和判断的依赖存在局限性和风险。随着 TBM 提供的大量机器数据以及数据分析和机器学习 (ML) 的进步，人们提出了许多模型。这些模型有可能通过提供更好的决策支持（包括地质预测和异常检测）来彻底改变隧道施工。然而，尽管这些模型有大量的研究和优点，但它们并未在现实场景中广泛实施。因此，本文旨在通过关注 TBM 隧道的地面预测模型来解决这个问题，全面概述当前最先进的技术，阐明现有实践，并强调阻碍这些模型被应用的局限性。隧道自动化的催化剂。通过强调这些挑战，本文不仅旨在提出批评，还旨在提供指导，为未来的研究提供建议，有望弥合差距，并有可能开创全自动隧道掘进机时代。