During tunneling processes, disc cutters of a tunnel boring machine (TBM) usually need to be frequently and unexpectedly replaced. Regular inspections are needed to check disc cutters’ status, which significantly reduces the work efficiency and increases the cost. This paper proposes a new prediction model based on TBM operational parameters and geological conditions that determines whether disc cutter replacement is needed. Firstly, an evaluation criterion for whether the cutters need to be replaced is constructed. Secondly, specific parameters related to the evaluation criterion are analyzed and 18 features are established on tunneling monitoring information. Then, the mapping model between the cutter replacement judgement and the established features is built based on a kernel support vector machine (KSVM). Finally, the data obtained from a Jilin water transport tunnel project is utilized to verify the performance of the proposed model. Test results show that the new model can obtain an average accuracy of 90.0% and an average F₁ score of 86.2% on field data prediction based on data from past tunneling days. Therefore, the proposed data-predictive model can be used in tunneling to accurately predict whether disc cutters need to be replaced before human judgment, and thereby greatly improve tunneling safety and efficiency.

In a tunneling boring machine (TBM), to obtain the attitude in real time is very important for a driver. However, the current laser targeting system has a large delay before obtaining the attitude. So, an adaptive-neuro-fuzzy-based information fusion method is proposed to predict the attitude of a laser targeting system in real time. In the proposed method, a dual-rate information fusion is used to fuse the information of a laser targeting system and a two-axis inclinometer, and then obtain roll and pitch angles with a higher rate and provide a smoother attitude prediction. Considering that a measurement error exists, the adaptive neuro-fuzzy inference system (ANFIS) is proposed to model the measurement error, and then the ANFIS-based model is combined with the dual-rate information fusion to achieve high performance. Experimental results show the ANFIS-based information fusion can provide higher real-time performance and accuracy of the attitude prediction. Experimental results also verify that the ANFIS-based information fusion can solve the problem of the laser targeting system losing signals.

Laser cleaning has received extensive attention due to its high efficiency, non-pollution and easy automation. However, how to improve the cleaning quality has become the focus of current research. In this paper, we used a pulsed laser for cleaning experiments on Q235B carbon steel to investigate the effects of different process parameters on the surface cleaning quality. On this basis, a new cleaning method was innovatively proposed to improve the oxide removal efficiency, microstructure, and mechanical properties of cleaned samples. The results showed that pulsed laser cleaning of Q235B carbon steel was the most effective at a laser linewidth of 50 mm, pulsed frequency of 500 kHz, and cleaning speed of 15 mm/s. A great deal of craters formed on the surface of cleaned samples due to the thermal shock of the pulsed laser. Compared with other laser cleaning methods, integrated laser cleaning had an obvious effect in raising the oxide removal efficiency and reducing the surface roughness. The ridge structures on the sample surface also could be successfully eliminated, subsequently achieving smooth structures. Fine-crystalline structures were formed near the surface of tested samples, which significantly decreased the crystal orientation and increased the number of small angle grain boundaries and the GND density. The improvement in hardness was mainly on account of grain refinement in the integrated laser cleaning samples. In addition, a physical model was proposed to illustrate the oxide removal mechanism on integrated pulsed-continuous laser cleaning samples. This research can offer new theoretical and technical support for solving the long-standing problems of efficiency and quality in laser cleaning, thus significantly broadening the application of laser technology in manufacturing fields.

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.