This paper presents the law of rockfall runout range and the effect on lateral dispersion caused by different parameters. A creative combined model test bench was developed as the test site, the slope, and blocks were artificial using similar materials that can meet the strength required in the experiment. As to the analysis of rockfall runout range, a series of experiments were carried out, and the shape, weight, and release heights of blocks were varied to clarify the effect of 3 factors. The motion trajectory of block was recorded based on a high‐speed camera and a defined coordinate system. In addition, the final longitudinal and lateral runout distances of block were also measured. Furthermore, an extended laboratory investigation was conducted, consisting of 160 tests with cubical blocks, and an empirical model was proposed from the test results that estimates the lateral dispersion W/L of cubical blocks with different mass and release height. For the design of practical mitigation measures, the research results in this paper may provide a useful reference.

The complexity of diseases in tunnel linings and the interference of clutter and the strong reflection of rebar in ground-penetrating radar (GPR) data are the important factors that lead to the low accuracy and poor automation of disease detection. As consequence, this paper carries out an automatic detection method for hidden lining diseases. Firstly, in order to suppress the interference of strong clutter, the state equation and measurement equation of GPR data are established, and the recursive formula of clutter suppression is deduced. Secondly, combined with a convolution neural network, the network which can suppress the strong reflection of rebar is built. Finally, the multi-dimensional characteristics of disease in the time domain, frequency domain, and time-frequency domain are extracted, and then the support vector machine (SVM) data set is established and the automatic detection method for diseases is formed. The proposed method can avoid the low efficiency of manual interpretation and the over-dependence of detection accuracy of relying upon the experience level of technicians.

Due to the particularity of loess engineering geology, loess railway tunnel accidents occur frequently. Based on the theoretical basis of risk management, this study evaluated the service performance of loess railway tunnels. Based on the improved TOPSIS method, the indirect proximity degree of each risk factor was compared, and the appropriate service performance evaluation index was selected. Based on the ISM model and the causality graph modification method, the dependency relationship between nodes was obtained and the Bayesian network evaluation model was constructed. By constructing the database, the EM algorithm was used for data learning, and the model was trained and verified, and the overall accuracy (ACC) and F₁ value are used to comprehensively evaluate the training and prediction effect of the model. Finally, the evaluation model was applied to a tunnel case. The results show that the established Bayesian network model has a high accuracy of 92%, which is easy to operate, effective and practical, and it is also applicable in situations of incomplete index statistical data.

This study focused on the detection of water inrush in tunnels excavated by full-section hard rock tunnel boring machines (TBMs) and employed ground penetrating radar methods for conducting research on radar signal processing algorithms. The research demonstrates that conventional techniques are inadequate for eliminating the interference of TBM equipment on radar signal propagation. This study employs a radar antenna array method for signal transmission, utilizing a wavelet double-threshold filtering algorithm and wave propagation theory to suppress clutter. These methods exhibit strong signal reception capabilities and are effective in eliminating 13.1% of the direct wave components. The adoption of a novel, efficient radar signal imaging algorithm simplifies the imaging process. Results of verification indicate that the synthetic aperture algorithm, enhanced with cross-correlation calculation, yields the optimal imaging effect. This investigation, which was conducted in conjunction with the construction of a diversion tunnel in a specific region, has confirmed the applicability of the ground penetrating radar method for the detection of water inrush in TBM tunnels by conducting a comparative analysis of the direct wave removal algorithm and the integration of the optimal imaging algorithm. The innovative application of ground penetrating radar within TBM tunnels, along with a targeted technology to mitigate signal interference from metal equipment, has led to the selection of an appropriate algorithm for both signal processing and imaging. This approach offers a novel solution for the detection of water source disasters in TBM tunnels.