Rock joints are important factors affecting natural disasters such as landslides, and weak interlayer is the decisive factor for rock joint stability. Therefore, it is necessary to carry out shear test research on rock joints with weak interlayer. In this paper, the red sandstone was used as rock joint material, and consistent saw-tooth rock joints were produced by the 3D carving technology. According to the on-site composition analysis, the weak interlayer used in the test was obtained through remodeling. Weak interlayer with different moisture contents and filling degrees were produced by the Constant Dry Mass Method (CDMM). Shear strength test of rock joint with weak interlayer of different filling degree and moisture content under multi-level normal stress was carried out by the self-developed direct shear apparatus. The test results show that peak shear strength of rock joint with weak interlayer increases firstly and then decreases with the raise of moisture content. Peak shear strength of rock joint with weak interlayer decreases firstly and then tends to a stable value with the raise of filling degree. As the filling degree of weak interlayer changes from small (less than 1.0) to large (more than 1.0), dilatancy mode of saw-tooth rock joint changes from dilatation to contraction during shearing. Peak shear strength of rock joint with weak interlayer increases with the raise of normal stress.

Rock Quality Designation (RQD) is among the widely used measures of the quality of rock masses and can be derived through Monte Carlo stochastic process-based fracture network simulations. However, repeated simulations can yield variable RQD results. Here, we introduce a four-step approach that incorporates class ratio analysis to estimate the representative RQD, which includes (1) extracting the mean and confidence interval of the RQD sample, in terms of the Confidence Neutrosophic Number Cubic Value (CNNCV), (2) employing class ratio analysis to determine the thresholds of the number of virtual boreholes and that of the number of models for a given size D, beyond which the CNNCV remains substantially unchanged, (3) accepting the CNNCV at the thresholds of the number of models as the representative RQD for the model of size D (RQD(D)) and (4) determining the representative RQD (rRQD), defined as the specific value which, once D exceeds, the RQD(D) does not change significantly. The introduced approach is illustrated with a case study of an open-pit slope in China, and it was tested for its performance. The RQD calculation results of the proposed method and the traditional single-model approach exhibit differences, which diminish with increasing model sizes. At the 95% confidence level, the stable size of the RQD determined by the proposed method is 13 m, compared to 25 m for the single-model approach. This method enhances the accuracy of representative elementary volume predictions by accounting for the diversity in the simulation results of RQDs for the same size. Overall, the introduced approach offers a reliable method for obtaining RQD estimates.

This paper presents a simple but workable constitutive model for the stress–strain relationship of sandy soil during the process of tunneling construction disturbance in coastal cities. The model was developed by linking the parameter K and internal angle φ of the Duncan–Chang model with the disturbed degree of sand, in which the effects of the initial void ratio on the strength deformation property of sands are considered using a unified disturbance function based on disturbed state concept theory. Three cases were analyzed to investigate the validity of the proposed constitutive model considering disturbance. After validation, the proposed constitutive model was further incorporated into a 3D finite element framework to predict the soil deformation caused by shield construction. It was found that the simulated results agreed well with the analytical solution, indicating that the developed numerical model with proposed constitutive relationship is capable of characterizing the mechanical properties of sand under tunneling construction disturbance.

In the construction of shaft, the blockage of the mucking shaft may cause the mud-water inrush disaster. Oversized rock fragmentation is the main cause for the blockage of the mucking shaft in the raise boring machine (RBM) construction method. The influence degree of blasting parameters on rock fragmentation after blasting is quantified by adopting analytic hierarchy process (AHP). On this basis, the shaft blasting maximum rock fragmentation control model based on double hidden layer BP neural network is proposed. Results show that the maximum rock fragmentation discharged from the mucking shaft after blasting should not exceed 1/3 of the diameter of the slag chute. The influence weight of the minimum resistance line that accounts to 15.6%, when AHP is applied for the quantification of the blasting parameters, can be regarded as the most important blasting parameter. The average absolute errors between the predicted value and the actual value of the largest block size control model of the shaft blasting are only 2.6%. The inversion analysis of the model can rapidly obtain the required blasting parameters, which can be used to guide the construction of the tunnel ventilation shaft.

The construction quality of tunnel smooth blasting is difficult to control and fluctuates greatly. Moreover, the existing technology, which relies on the visual observation, empirical judgment, and artificial control, has difficulty meeting the requirements of tunnel smooth blasting construction quality control. This paper presents the construction principle of a tunnel smooth blasting quality control system, introduces a process quality control technology into quality control of tunnel smooth blasting construction, designs a framework for the tunnel smooth blasting quality control system, and collects control index data based on field investigation, expert consultation, and experimental research. By using the methods of index utilization rate statistics, gray correlation analysis, and principal component analysis, this paper primarily elects and selects the control indexes; establishes the tunnel smooth blasting quality control index system; constructs a comprehensive optimization control model of tunnel smooth blasting quality using back propagation artificial neural network (BP-ANN), Elman neural network (ENN), and adaptive neuro fuzzy inference systems (ANFIS); and studies the tunnel smooth blasting quality control system. The following are the conclusions of this study: (1) This paper presented a method of constructing a tunnel smooth blasting quality control index system and established this system with seven criteria layers, namely, geological conditions, explosive properties, borehole parameters, charge parameters, method of initiation, tunnel parameters, and construction factors, as well as a total of nine indexes. (2) The comprehensive optimization control model of BP-ANN, ANFIS, and ENN for tunnel smooth blasting quality was established. (3) The uniform design method was used to optimize the blasting parameters of the tunnel section, which needs to be controlled, and verify that the construction of these comprehensive optimization control models can change the focus of tunnel smooth blasting quality control from the traditional single index control method into a dynamic, intelligent, pluralistic, and integrated control technology.