The proportion of universal segment in tunnel construction is constantly increasing. A key factor affecting the quality of tunnel construction is the selection of the shield segment assembly points. Nevertheless, the quality and efficiency of the current manual selection method cannot be guaranteed. To realize a high correct rate, high efficiency and intelligence of universal segment assembly points selection, an intelligent selection method of assembly points is proposed. First, the objective function is established by considering the thrust cylinder stroke and shield tail gap differences. Second, to adaptively optimize the weights of the objective function, the working conditions are divided into 81 intervals, and a genetic algorithm is proposed to optimize weights in each interval. Third, a Monte-Carlo-based method is proposed to generate an example dataset, which is used for the genetic algorithm to optimize the weights. Finally, the proposed method was applied to the segment assembly points selection for Line 8 of the Zhengzhou rail transit in China. The results show that the method of assembly segment selection can reach a 90.6% correct rate in the field. The research results of this paper can be used for the selection of the universal shield segment assembly points.

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.

In addressing the challenge of measuring the relative pose between adjacent shields in multi-shield tunnel boring machine (TBM), a multi-sensor integrated measurement method combined with artificial neural networks (ANNs) is proposed. The adjacent shields, along with the articulation cylinders, collectively form a parallel mechanism. While solving for the relative pose between the shields, the parallel arrangement of the articulation cylinders results in insufficient constraints provided by the lengths of the cylinders. After analyzing the sensitivities of changes in the articulation cylinder strokes and relative pose parameters, additional sensors, including a photosensitive target, a collimated laser, and two inclinometers are added. These are integrated with the articulation cylinder displacement sensors to form the sensing system. An ANN model is established to calculate the relative pose between adjacent shields based on data from these sensors. The required dataset for training the ANN is obtained through the kinematic inverse solution of the parallel mechanism. The proposed method was validated on emulation test platform for pose measurement. Compared to the existing methods, it achieved higher accuracy and stronger environmental adaptability to dust and fog interference. The proposed method was also applied to a 6m-class double-shield TBM, it achieved an accuracy of 4 mm for relative positions and 1 mrad for relative attitudes, meeting the guidance requirements for multi-shield TBM.

Shield tunneling is the method most commonly used for underground projects. Segment typesetting, which involves determining the optimal assembly point for