Rockburst hazards occur sporadically after excavation of deep-buried hard rock tunnel. These failures in the surrounding rock masses are primarily induced by high ground stress, rendering conventional rock mass quality classification systems less applicable. This study discusses the limitations of existing rock mass quality classification systems when applied to deep-buried hard rock tunnels. A rockburst intensity tendency index, quantified through microseismic (MS) monitoring, is introduced and integrated into the RMR system, resulting in the development of an engineering rock mass quality classification system for deep-buried hard rock tunnels (DHRT-RMR). The development process involves: (i) selecting input parameters, including the rockburst intensity tendency index, and defining their weightings using the AHP; and (ii) establishing the DHRT-RMR system based on the principles of the RMR system. The rockburst intensity tendency index, DHRT-RMR system, and RMR system are then applied to two test sites selected from a tunnel in southwest China. Results indicate that the standalone use of RMR or the rockburst intensity tendency index is limited in engineering rock mass classification for deep-buried hard rock tunnels. However, the DHRT-RMR system can accurately assesses rock mass qualities in such tunnels.

Choosing reasonable control measures for different intensity rockburst risks not only effectively prevents and mitigates rockburst risks but also reduces time and engineering investment costs. Due to the limitations of the tunnel boring machine’s structure and working conditions, tunnels excavated by TBMs are highly susceptible to rockbursts. What is even worse is that there are very few measures to control the rockburst risk in these tunnels. Implementing reasonable control measures from the limited mitigation measures to control and mitigate rockburst in TBM tunnels is an urgent problem that warrants a solution. In this paper, a large number of on-site rockburst risk control cases and a large amount of MS monitoring data (the total mileage of MS monitoring is approximately 7 km, lasting for 482 days) are used to derive a reasonable scheme to control the rockburst risk of different intensities in twin TBM tunnels. First, the rockburst early warning effect of the two headrace tunnels of the Neelum–Jhelum hydropower station based on microseismic monitoring is analyzed. Second, based on highly accurate rockburst warning results, 94 rockburst risk control cases are applied to analyze the control effect of different control measures at different intensities of rockburst risk. Then, by combining factors such as the time cost and expense cost of different control measures, more reasonable control measures for different intensity rockburst risks are proposed: for slight rockburst risk, normal excavation is preferred; for moderate rockburst risk, horizontal destress boreholes are preferred; and for intense rockburst risk, a combination of measures of shortening daily advance and horizontal destress boreholes is preferred. The research results can provide a reference for other TBM excavation projects to carry out rockburst risk prevention and mitigation.

In deeply buried, small-clearance tunnels, the failure of the surrounding rock is profoundly influenced by the superposition of stresses and the cumulative disturbance effects from multiple blasting events. Consequently, the failure characteristics and mechanisms of the surrounding rock are highly complex. Through a comprehensive analysis encompassing failure investigations, geological assessments, and surrounding rock pressure monitoring, this study systematically examines the spatio-temporal failure characteristics and geological discrepancies across 3 parallel tunnels (namely, a pilot tunnel, a left tunnel, and a right tunnel). The analysis reveals the asymmetric failure behavior of the surrounding rock and offers a detailed discussion of the underlying mechanisms. The temporal and spatial evolution of the surrounding rock pressure in these tunnels is carefully analyzed, with an emphasis on uncovering the asymmetric failure mechanisms during the excavation of deep, small-clearance tunnels. The results demonstrate that the failure of the surrounding rock exhibits significant asymmetry during excavation, with the damage being more pronounced on the valley side compared to the mountain side. Furthermore, the degree of damage in the advance tunnel is substantially greater than that in the backward tunnel, particularly in sections following the excavation of the backward tunnel. Additionally, the distribution of the surrounding rock pressure in the advance tunnel also exhibits pronounced asymmetry. The asymmetric failure of the surrounding rock is primarily attributed to the stress concentration in the deep valley and the disturbances introduced by the excavation process, which induces tangential stress concentrations in the surrounding rock mass. The findings of this study hold considerable significance for the design and optimization of tunnel support systems, as well as for disaster prevention strategies in deeply buried, small-clearance tunnels.

Aiming at the problems of microseismic signals from rock mass fracture in deep-buried tunnels with low signal-to-noise ratio (SNR) suffering from coupled interference of multi-source noise, and traditional filtering methods having fixed parameters and poor processing effects on spectral aliasing, this study proposes a ternary coupled adaptive filtering method integrating the Sparrow Search Algorithm, Variational Mode Decomposition and Wavelet Threshold Denoising (SSA-VMD-DWT). First, the noise intrusion characteristics of low-SNR microseismic signals in deep-buried tunnels were analyzed, and the filtering difficulties of white noise, low-frequency noise, high-frequency noise and non-stationary noise were clarified. Subsequently, a parameter optimization framework with the Sparrow Search Algorithm (SSA) as the core was constructed to optimize the key parameters, including the penalty factor α and modal number K of Variational Mode Decomposition (VMD), as well as the wavelet basis and decomposition layers of Wavelet Threshold Denoising (DWT), respectively. A dual-index threshold decision function based on kurtosis and correlation coefficient, and a wavelet packet entropy weighted reconstruction algorithm were designed to realize the collaborative adaptive adjustment of decomposition depth and threshold rules. Finally, the performance of the algorithm was verified through simulation signal experiments and an engineering case of a deep-buried tunnel in Southwest China. The results show that for the simulated signal with a low SNR of 2 dB, the SNR is increased to 12.43 dB, and the root mean square error is reduced to 2.36 × 10⁻⁷ after denoising by this algorithm, which is significantly superior to the Empirical Mode Decomposition (EMD) and traditional DWT methods. In the engineering case, the information entropy of the filtered signal is the lowest among all methods, which can effectively suppress multi-band noise and retain the core characteristics of microseismic signals from rock mass fracture, solving the problems of spectral aliasing, detail loss and empirical parameter setting of traditional methods. This method provides a new technical paradigm for the processing of low-quality microseismic signals in deep tunnel engineering and can improve the accuracy of monitoring and early warning for rock mass dynamic disasters.