Shield machine performance and tunnelling-induced settlement are the main concerns during the tunnelling process. This study proposes an artificial intelligence Internet of Things (AIoT)-based system for real-time monitoring of tunnel construction. Shield machine operational parameters and tunnelling-induced settlement can be transferred and stored in real time by an AIoT system. Thereafter, shield operational parameters and tunnelling-induced settlement prediction models based on machine learning algorithm random forest (RF) are established based on the collected data. The models are further employed to predict shield operational parameters and ground response at the next step. This dynamic system was applied to a practical tunnel engineering. The results indicate the implementation of such process from the data collection, training and updating of RF-based models, and decision making of controlling shield machine performance can be completed within 15 minutes, which is much less than the time of excavating and installing a segmental ring, ensuring the real-time control of shield machine. Based on the predicted shield operational parameters, maximum and mean prediction error of the tunnelling-induced settlement can be controlled within 5 and 2.5 mm, respectively. The AIoT-based system improves the information and automation level during the construction process, facilitates decision-making and avoids accidents.

盾构机的性能和隧道掘进引起的沉降是隧道掘进过程中的主要问题。这项研究提出了一种基于人工智能物联网（AIoT）的系统，用于隧道施工的实时监控。盾构机的运行参数和隧道引起的沉降可以通过AIoT系统实时传输和存储。此后，基于收集的数据，建立了基于机器学习算法随机森林（RF）的盾构运行参数和隧道诱发沉降预测模型。该模型进一步用于预测下一步的屏蔽操作参数和地面响应。该动态系统已应用于实际的隧道工程。结果表明从数据收集中执行了该过程，基于RF的模型的训练和更新以及控制盾构机性能的决策可以在15分钟内完成，这比挖掘和安装分段环的时间要少得多，从而确保了盾构机的实时控制。根据预测的盾构运行参数，可以将隧道引起的沉降的最大和平均预测误差分别控制在5毫米和2.5毫米之内。基于AIoT的系统在施工过程中提高了信息和自动化水平，有助于决策制定并避免发生事故。根据预测的盾构运行参数，可以将隧道引起的沉降的最大和平均预测误差分别控制在5毫米和2.5毫米之内。基于AIoT的系统在施工过程中提高了信息和自动化水平，有助于决策制定并避免发生事故。根据预测的盾构运行参数，可以将隧道引起的沉降的最大和平均预测误差分别控制在5毫米和2.5毫米之内。基于AIoT的系统在施工过程中提高了信息和自动化水平，有助于决策制定并避免发生事故。