Search Results (334)

The southeastern coastal region of China is extensively influenced by the circum-Pacific geothermal activity, particularly during the excavation of deep-buried tunnels, where the confined space leads to the accumulation of heat flow, resulting in high-temperature and high-humidity environments. These conditions are detrimental to both the physical and mental health of workers and the safe operation of equipment. Based on this, the Lijiashan deep-buried high-temperature tunnel along the Wen-Yu High-Speed Railway (Wenling-Yuhuan) was selected as a case study. Field monitoring was conducted to assess the surrounding rock stress, temperature distribution characteristics of the surrounding rock and structure, and the humid and high-temperature environment within the tunnel during construction. A comprehensive evaluation index considering both temperature and humidity was employed to evaluate the tunnel construction environment. The results indicate the following: (1) During tunnel excavation, the maximum surrounding rock pressure occurs at the arched shoulder, and the fractures induced by blasting effectively relieve stress, mitigating the risk of rockburst. (2) The seepage paths of the surrounding rock are redistributed during excavation, converging towards the invert, with the osmotic pressure being approximately 10 times that of the upper structure. (3) The temperature at the tunnel face, secondary lining, and surrounding rock is significantly influenced by the heat released from concrete hydration. The closer the surrounding rock is to the support structure, the higher the temperature, with the secondary lining reaching up to 58.6 °C and the working area up to 35.2 °C. (4) Water spraying can reduce the temperature in the construction area by approximately 0.65% at the Kelvin temperature conditions, but it increases humidity by about 16%. The average humidity levels within the tunnel are 75.3% during the day and 87.5% at night. (5) Evaluation of workers’ physiological parameters reveals that the humid and high-temperature environment during tunnel construction is consistently unfavorable for workers’ health. Full article

The high-pressure spherical gas storage tank in a wind tunnel energy storage and gas supply system is a critical pressure-bearing component of the wind tunnel operation system. The linearized stress in its critical control region is a key parameter for structural safety assessment. Therefore, investigating and evaluating the linearized stress and its associated uncertainty in this region is essential for enhancing operational safety. In this study, a three-dimensional finite element model of the spherical tank was developed, and the critical control region was identified through stress linearization. The operating internal pressure, working temperature, and shell wall thickness were treated as random input variables. Based on the stress linearization results, the stability of the critical control location was assessed. For physically homogeneous intervals, a non-intrusive polynomial chaos expansion surrogate model was constructed, and a conditional uncertainty propagation model for the linearized stress was established. Compared with the Monte Carlo and GUM methods, the non-intrusive polynomial chaos expansion method achieves substantially higher computational efficiency while producing consistent evaluation results. The uncertainty analysis shows that the operating internal pressure is the dominant contributor to the uncertainty of the linearized stress, followed by the effective wall thickness of the spherical shell. In contrast, the working temperature has a minor effect, and the interactions among the input variables are weak. Full article

Roof stability plays a crucial role in maintaining the overall stability of surrounding rocks to ensure safety of tunnel construction and operation. In this work, tension cut-off (TC) technique is introduced to modify the Hoek–Brown (HB) criterion to describe the tensile failure of rock strata. Thereafter, stability analysis of anchor bolt-reinforced tunnel roofs in rock strata subjected to a hybrid tensile-shear fracture is performed. The work balance equation is established by equating the external work rates of the falling block and the anchor bolts to the internal energy dissipation rate. Two stability indicators, that is the stability number (N) and the factor of safety (FoS) are proposed to quantitatively analyze the stability of tunnel roofs. Optimization algorithms combining genetic algorithm and particle swarm optimization are programmed to capture the optimal upper bound solutions. The influences of TC, strength criterion parameters, and anchor bolt-reinforcement strength on roof stability are explored in this work. It was found that increasing the anchor tension T improves the FoS of reinforced tunnel roofs, with an increase of up to 68% observed for rectangular tunnel roofs under the selected representative case, while the improvement is relatively less pronounced for circular tunnel roofs. Regarding anchor support, as ξ increases, the N for rectangular tunnels nearly doubles. This work provides a theoretical basis for preliminary designing of tunnels in reinforced rock strata. Full article

As tunnel construction advances toward greater depths and lengths, full-face tunnel boring machines (TBMs) have become the preferred method for large-scale excavation. The operational efficiency of TBMs significantly impacts the progress, cost, and safety of tunnel projects. With the rapid development of machine learning and big data technologies, data-driven models based on cutterhead response signals have emerged as a key approach to improving TBM perception and decision-making. However, conventional rock-breaking indicators predominantly rely on single physical variables, limiting their ability to capture the complex dynamic interactions between the TBM and surrounding rock during excavation, thereby restricting their engineering applicability. To address this limitation, this study proposes a knowledge-driven data processing and indicator construction method to more accurately represent TBM operational states and surrounding rock properties. First, a novel excavation phase division algorithm based on time-domain and penetration-depth features is developed to accurately distinguish different tunneling stages. Subsequently, using data from the YC and YE projects, thrust- and torque-driven rock-breaking indicators are formulated, and the relationship between penetration depth and thrust/torque is optimized via power function fitting. Optimal exponents are determined through algorithmic optimization. Validation with field data confirms that the proposed indicators significantly enhance the accuracy and generalization of surrounding rock classification and control parameter prediction models. Full article

Split-type robots are increasingly deployed in unstructured confined environments such as underground coal mines, where autonomous navigation and cooperative tracking control remain critical challenges. This paper presents a visual target-assisted tracking control scheme for a split-type drilling robot, adopting an active leader–passive follower architecture. The leader robot performs autonomous mobility and obstacle avoidance using 3D LiDAR-based offline path generation and online optimal search. The follower robot uses AprilTag visual fiducial markers to estimate the six-degree-of-freedom relative pose via the Perspective-N-Point algorithm, and it tracks the leader using a two-dimensional fuzzy PID controller that adaptively tunes PID parameters. Extensive experiments are conducted in simulation, simulated tunnels, a large-scale robot platform, and a real drilling robot prototype. Results demonstrate that the leader achieves an average navigation error below 0.175 m, while the follower maintains an average relative tracking error within 0.06 m. The proposed method enables stable, comparable accuracy with smoother, less oscillatory response, and high-precision cooperative navigation for heavy-duty split-type robots, offering a practical solution for intelligent drilling operations in underground confined spaces. Full article

Decision-making during shield tunneling remains challenging due to complex shield–ground interactions, and experience-driven adjustments to shield operating parameters often fail to balance tunneling efficiency and energy consumption. For this purpose, a rule-constrained multi-objective optimization framework for shield operating parameters is proposed and validated using field data from a slurry shield tunneling project in Changsha, China. Here, a comprehensive field dataset is established by integrating ground conditions, operating parameters, and energy consumption indicators. Association rule mining is employed to identify typical combination patterns of operating parameters under different ground conditions, which are included as feasibility constraints in the parameter optimization. The relationship between operating parameters, tunneling efficiency, and energy consumption is captured by a random forest model, which serves as a surrogate model for rapid evaluation of operating parameters. Therefore, the NSGA-II algorithm is employed to obtain Pareto-optimal parameter combinations under feasibility constraints. The results indicate that the proposed framework can provide adaptive optimization strategies under different ground conditions. The resulting Pareto solutions can be classified into three tunneling modes, including robust, balanced, and high-speed, facilitating practical decision-making for shield operators. Full article

Accurate path-loss models are essential for planning reliable wireless networks in underground mines, yet existing characterization studies rely on specialized channel sounders and vector network analyzers costing tens of thousands of dollars, placing them beyond the reach of most mine operators. This paper demonstrates that LoRa transceivers costing approximately US $15 per node can serve as a self-contained path-loss measurement instrument, logging the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) directly to a CSV file over a standard USB serial connection. A measurement campaign conducted at the Missouri S&T Experimental Mine on 31 March 2026 collected 4801 packets across four distinct underground canonical primitives: straight tunnel, T-junction, vertical shaft, and post-bend NLoS gallery at distances of 5 to 60 m using Waveshare Pico-LoRa-SX1262 boards operating at 915 MHz. The results reveal a pronounced two-zone propagation structure, including a line-of-sight (LoS) zone with a negative path-loss exponent of −0.34, confirming tunnel waveguide gain up to 25 m, followed by a steep NLoS zone with an exponent of 13.0 after a 24.0 dB bend diffraction loss. Environment-specific measurements quantify a 5.5 dB junction excess loss and a 29.5 dB shaft excess loss relative to a straight-tunnel reference. Spreading factor sensitivity tests across SF7, SF9, and SF12 confirm that RSSI measurements are consistent to within 2 dB across all SFs, validating the measurement methodology. The resulting four-zone path-loss model provides mine network planners with parameters sufficient for LoRa link budget design and relay node placement without any specialized RF instrumentation. Full article

Roof instability in the heading area of fully mechanized excavation roadways, together with insufficient coordinated operation between excavation and support, severely restricts tunneling safety and construction efficiency. A novel track-mounted advanced support equipment structure with an articulated curved roof beam is proposed in this study. Considering actual underground working conditions, including uneven roof contact, eccentric loading and local support failure, a three-degree-of-freedom dynamic model covering vertical, pitch and roll motions is established based on Lagrange’s equations. Dynamic characteristics under varying load amplitudes, excitation frequencies, static load offsets and typical support failure modes are systematically analyzed. The results reveal that only vertical vibration emerges under the full support condition, and the resonance frequency of the system is approximately 10 Hz. The maximum steady-state vertical displacement reaches 0.6406 mm with an RMS of 0.5472 mm under an intact support state. The pitch vibration amplitude caused by the failure of the first support group is three times that of the second group, proving front supports dominate anti-overturning capacity. Side beam failure triggers remarkable roll-coupled vibration, while middle beam failure mainly enlarges vertical displacement. This paper clarifies the vertical–pitch–roll coupling vibration mechanism induced by local support failure. Parameter sensitivity analysis reveals that static load offset has the highest sensitivity, while excitation frequency (within 4–6 Hz) and damping ratio exhibit negligible influence on the steady-state response. The obtained quantitative results can provide a reliable theoretical reference for structural optimization, stability regulation and safety monitoring of track-mounted advanced support facilities. Full article

The integration of computational fluid dynamics (CFD) with deep learning in tunnel fire research is currently constrained by excessive reliance on manual operations and low overall efficiency. To address these limitations, this study presents a multi-agent collaborative framework driven by large language models, which enables full automation of the fire source characteristic inversion process. This framework reorganizes the conventional research pipeline into four dedicated, specialized agents: physical modeling, data governance, model training, and evaluation analysis. As a typical automated verification task, five deep learning models are systematically benchmarked under 45 experimental configurations to implement multi-task continuous regression inversion, which fully demonstrates the framework’s capability of automated, reproducible and large-scale comparative experiments. The experimental results demonstrated that the CNN-LSTM model outperforms other models in extracting spatiotemporal correlation features from temperature time-series data, enabling high-precision prediction of multiple fire parameters. With a 6 s observation window and 10 m sensor spacing, the average R² attains 0.942, an improvement of 2% over the baseline LSTM model, and the RMSE decreases by 28.8%. For sparse sensor deployment at 30 m spacing, the average R² remains at 0.917, confirming the effectiveness of integrating spatial feature extraction with temporal modeling. This study provides an efficient technical pathway for intelligent tunnel fire identification and advances the research paradigm by shifting the traditional manual optimization process to a multi-agent system-based optimization workflow. Full article

To improve the accuracy of cutter wear and service life prediction for disc cutters, an improved normal force model is established based on the traditional CSM model by considering the supporting force and friction acting on the disc cutter from the side crushing zones. By incorporating the micro-mechanism of abrasive wear, an analytical model for the radial wear of the disc cutter and a service life prediction model are derived. Meanwhile, a regression model for cutter wear is established based on field operational parameters and cutter wear data. The mechanistic model is validated using field data from a tunnel project in Guangdong, China, and the results show that the average prediction errors of wear and service life are 8.13% and 8.85%, respectively, which are significantly lower than those of the traditional CSM model. Further comparative analysis between the two types of models is conducted, and the results indicate that the regression model achieves average prediction errors of 7.57% and 7.86% for wear and service life, respectively, showing higher prediction accuracy than the mechanistic model. The results demonstrate that the mechanistic model is suitable for revealing the wear mechanism of the disc cutter, while the regression model is more applicable for engineering prediction, and the two approaches can be used in a complementary manner. Full article

The conventional double-sidewall pilot tunnelling method (CDWM) has been widely applied in the construction of large-span tunnels. However, when applied to shallow-buried tunnels in weak surrounding rock, the method often suffers from excessive deformation and complex support conversion. To address these limitations, this study proposes an optimized excavation scheme, namely the double-sidewall pilot tunnelling method with reserved rock walls (DRWM). The Yangjiashan Tunnel in Zhejiang Province, China, was selected as the engineering case for investigation. Field monitoring data and numerical simulations were integrated to evaluate the deformation behaviour and structural response of the tunnel during excavation. The results indicate that DRWM significantly improves deformation control compared with the conventional CDWM. The maximum crown settlement and horizontal convergence were effectively reduced, and stress concentration in the initial support structure was mitigated. Furthermore, a sensitivity analysis was conducted to investigate the influence of the lateral distance between the temporary support and the reserved rock wall. Within the investigated single-parameter analysis, a spacing of 0.4–0.5 m showed a relatively balanced response in terms of crown settlement, horizontal convergence, support stress, and construction operability. The findings demonstrate that, under the investigated Grade V weak rock conditions, the DRWM showed improved deformation control compared with the CDWM in the numerical comparison, highlighting its potential applicability for optimization in comparable engineering settings. Full article

In hot and humid regions, urban underground utility tunnels are susceptible to high temperature and humidity due to moist inlet air, cable heat dissipation, and limited ventilation jointly affecting the internal environment. To address this issue, an alternating ventilation strategy, in which fan operation is periodically reversed to switch between air supply and exhaust, is proposed. Compared to conventional mechanical ventilation, this strategy overcomes the constraints of unidirectional airflow and mitigates thermal and humidity stratification, with low retrofit requirements and good adaptability. Ventilation performance was evaluated using non-guarantee rates for temperature and relative humidity, i.e., the ratio of the number of measurement points where the temperature/relative humidity exceeds 40 °C/65% to the total number of measurement points in the utility tunnel (TNGR and RHNGR), non-uniformity coefficients (K_T and K_RH), and mean temperature (T_m). The alternating mode outperformed the conventional mode, reducing TNGR by 6.0% and Tm by 0.3 °C while improving temperature and humidity distributions and lowering cable temperatures. Although the reduction in T_m appears modest, it is practically meaningful because it helps weaken thermal stratification and local overheating, improves cable operating conditions, and may reduce the need for high-airflow operation when tunnel temperatures approach the permissible limit. Response surface methodology was further used to optimize the alternating ventilation parameters, indicating that the recommended fan commutation frequency is 2 under different inlet air temperatures. CFD validation confirmed the effectiveness of the optimized scheme. At an inlet air temperature of 35 °C, KRH decreased from 11.9% to 11.0% and Tm decreased from 37.5 °C to 36.9 °C. Full article

Fluctuating pressure generated during ship operation is closely related to propeller vibration, noise, and structural safety, and its frequency spectrum is a key design indicator in the propeller design stage. However, water tunnel experiments to measure fluctuating pressure generated by high-speed propellers require high-pressure facilities and involve complex procedures, high costs, and long lead times when experimental conditions are modified or new propellers are tested. To overcome these limitations, this study proposes a deep learning-based network, referred to as MSPFS-Net (Model-Test Ship Pressure Frequency Spectra Network), to estimate the frequency spectrum of fluctuating pressure from model test data. The proposed method uses propeller CAD data, principal design parameters, wake data, and water tunnel test conditions as inputs, and is trained in a supervised learning framework using frequency-domain data obtained by transforming experimentally measured fluctuating pressure signals. The trained network can predict the fluctuating pressure frequency spectrum without direct sensor measurements, even under conditions not present in the training dataset. The results of this study demonstrate the potential to reduce dependence on water tunnel experiments and to efficiently evaluate fluctuating pressure characteristics in the early design stage, indicating that the proposed approach can serve as a practical design support tool in terms of both cost and time efficiency. Full article

Deformation monitoring is critical for structural health assessment of operational shield tunnels in urban rail transit. LiDAR point clouds in operating tunnels usually contain auxiliary facilities, occlusions, noise, and uneven point density. Conventional section-by-section ellipse fitting often leads to unstable parameter jumps between adjacent sections. This paper presents a high-precision method to extract lateral deformation from tunnel LiDAR point clouds. First, a point-wise attention Transformer network (PWAT) is proposed based on PointNet++ for lining segmentation, using k-NN adaptive sampling, geometric position encoding, and geometry-constrained multi-head self-attention. Second, a continuity-constrained RANSAC (CC-RANSAC) algorithm is developed to improve ellipse parameter stability by adding continuity penalties between neighboring sections. Experiments were carried out on a Shanghai metro shield tunnel. Results show that PWAT achieves 99.53% overall accuracy and 99.06% mIoU in six-class segmentation. CC-RANSAC reduces the mean residual to 2.0 mm and the center jump rate to 4.2%. Compared with total station data, the mean absolute error and root mean square error are 1.35 mm and 1.68 mm. The proposed method can automatically and accurately extract lateral deformation for operational shield tunnels. Full article

This study systematically optimized the geometric parameters of three typical cross-sections for curved-wall highway tunnel inner contours. Aiming to minimize the net excavation area, a unified genetic algorithm-based optimization framework was established for systematic comparison of four typical curved-wall section types and implemented on the Matlab(R2023b) platform, incorporating encoding, selection, crossover, and mutation operations for global optimization of geometric parameters across different section types. The optimized sections were further validated for structural performance using Midas GTS NX. Results show that the proposed multi-type optimization framework effectively reduced tunnel excavation areas across all section types, with mean optimization rates of 2.60% ± 0.21%, 2.11% ± 0.03%, 4.70% ± 0.02%, and 2.54% ± 0.02% (95% CI) achieved for single-circle, triple-circle Model-1, triple-circle Model-2, and five-circle sections, respectively, providing quantitative evidence for section-type selection in highway tunnel design. In terms of structural performance, the optimized sections demonstrated favorable axial force and bending moment characteristics. The findings provide a quantitative basis combining economic efficiency and structural rationality for tunnel section design, offering significant engineering application value and technical support for standardized and refined highway tunnel design in China. Full article