Search Results (90)

Underground gas control is a critical link in coal mine safety production, and drilling robots serve as the core equipment for gas extraction drilling operations. However, the autonomous locomotion technology of coal mine drilling robots has long been constrained by the unstructured underground environment and the limitations of existing navigation schemes, which restrict their intelligent application. To address this bottleneck, this paper conducts systematic research on key autonomous navigation technologies for coal mine drilling robots operating in narrow underground working faces, focusing on their practical operational requirements. First, a hardware scheme complying with coal mine safety standards is selected, the hardware structure and sensor layout are optimized via digital modeling, and the software interface and data interface format of the navigation system are designed. Second, an innovative 3D point cloud-based offline obstacle avoidance algorithm is proposed, which integrates a terrain analysis module, a local path planning method with maximum arrival probability, a Bézier curve-based trajectory library generation strategy, and a trajectory index construction method. Finally, simulation experiments, ground-simulated roadway field tests, and underground coal mine field experiments are performed to validate the proposed system. Experimental results demonstrate that the constructed autonomous navigation system enables smooth and safe autonomous locomotion and fixed-point parking of drilling robots, with an average parking error lower than 0.17 m, and can effectively avoid obstacles in complex environments. This research provides crucial technical support for the intelligent advancement of coal mine drilling robots. 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

Rock bolt corrosion can weaken support systems and affect the long-term stability of water-flooded roadways. This study investigates the symmetry evolution of roadway deformation induced by bolt deterioration in alkaline water-flooded roadways, using Sanxin Coal Mine, Xinjiang, as a case. Electrochemical accelerated corrosion tests were conducted in 10% Na₂SO₄ solutions at pH = 9, 11, and 13 for 3, 6, and 9 d, followed by uniaxial tensile tests and FLAC3D numerical simulations. Under the controlled accelerated electrochemical conditions, the mass loss rate and corrosion rate generally increased with corrosion duration, with the greatest deterioration observed in the pH = 13 group after 9 d. The tensile curves of corroded bolts still exhibited elastic deformation, yielding, strain hardening, and post-peak softening stages. However, the yield load decreased with increasing mass loss rate, with fitted slopes of −0.1842, −0.07531, and −0.04998 kN/% for pH = 9, 11, and 13, respectively. Numerical results showed that bolt deterioration intensified roadway deformation and stress redistribution. Under severe corrosion, the horizontal displacement of the two sidewalls reached approximately −153.7 mm and 155.4 mm, while the maximum roof subsidence and floor heave reached about −188.7 mm and 191.3 mm, respectively. The shallow stress release zone expanded, and the deep stress concentration became more pronounced. Moreover, bolt deterioration intensified the roadway response while largely preserving its left–right symmetry. The numerical results incorporating the experimentally derived bolt deterioration showed increased roadway deformation and stress redistribution, indicating that bolt-capacity degradation can adversely affect roadway stability. These findings provide a reference for evaluating residual support performance and designing reinforcement measures for water-flooded roadways. Full article

Road geometry is a modifiable determinant of crash occurrence and severity; addressing it is critical for achieving sustainable transport systems. Yet, policy action requires clear prioritization across road types and years to ensure sustainable resource allocation. This study analyzes fatal and injury outcomes by roadway geometric context in Türkiye (2015–2024) and proposes a cell-level prioritization framework integrating crash burden, severity, and short-term deviations to support long-term sustainable road safety management. Annual data were structured as Year × Road type × Geometry × Category, with severity measured as deaths and injuries per 100 crashes (Kmin = 30). A Road Geometry Severity Index (RGSI; 0–100) combined standardized severity, log crash burden, and deviation from a three-year baseline. Isolation Forest and a MAD-based rule identified anomalies, while K-means clustering (K = 4) revealed burden–severity profiles. Results show deaths per 100 crashes declined from 7.91 (2015) to 3.29 (2022), then rose to 6.22 (2024). Severity was highest on provincial (8.82) and state roads (7.23), compared to motorways (4.66). High-severity cells were dominated by provincial-road contexts, especially dangerous curves and junction-related categories. The highest-priority cell was 2018–Provincial Road–Junction–No Junction (RGSI = 100). Under the predefined contamination specification (γ = 0.05), the Isolation Forest model flagged 35 anomalous cells, all of which also satisfied the MAD-based anomaly criterion. Findings highlight persistent high-priority roadway geometric contexts and demonstrate the potential of RGSI as a transparent infrastructure-prioritization tool. Full article

Mine disasters require urgent lifeline setup in confined tunnels, but manual rescue in unstable accident zones carries huge safety risks. Coal mine rescue robots (CMRRs) have become key equipment to replace manual rescue. However, traditional remote-controlled CMRRs suffer from low autonomy and weak environmental perception capability, which have become critical bottlenecks for field application. As an emerging technology in the mining field, digital twin enables high-precision virtual-real mapping and on-site operation guidance, providing a novel solution to the above problems. To realize autonomous navigation and digital twin visualization of the CMRR, this paper first carries out targeted hardware retrofits on the CMRR platform, upgrades environmental perception, communication transmission and motion control modules, and lays a solid hardware foundation for subsequent algorithm design and system implementation. Aiming at the complex post-disaster underground environment, a digital twin-integrated CMRR system is constructed. For intelligent autonomous navigation, this study investigates a 3D point cloud–based autonomous navigation framework and proposes a slope-fitting method as well as a maximum arrival probability obstacle avoidance method based on Bézier curve trajectories. For environmental visualization, a digital twin interactive interface is built to monitor gas and other environmental parameters in real time, and accurately reconstruct underground roadway structures based on point cloud data. This design not only ensures the robot’s autonomous obstacle avoidance but also helps rescuers grasp underground conditions in advance. Field tests in a simulated post-disaster mine with complex terrain show that the system can stably complete autonomous navigation tasks, maintain stable motion control under dynamic interference, and provide accurate and reliable environmental data for rescue decisions, verifying its feasibility and effectiveness in harsh mine rescue scenarios. Full article

To further improve the mechanical properties of shotcrete in coal mine roadways, end-hooked steel fibers and chopped basalt fibers were selected. Based on the optimal mix ratios identified in existing research, steel–basalt hybrid fiber shotcrete (SBFC) specimens were prepared. Dynamic impact tests under different impact loads and various hybrid fiber contents were conducted using an SHPB. The microstructure was characterized using SEM and XRD. The results show that the dynamic compressive stress–strain curve of steel–basalt hybrid fiber shotcrete can be classified as elastic deformation stage, plastic yield stage, and post-peak failure stage. The incorporation of hybrid fibers reduces the elastic deformation and plastic yield stage, and the post-peak failure stage under active confining pressure shows elastic aftereffect characteristics. The dynamic compressive strength, dynamic elastic modulus, and deformation modulus increase with the increase in impact pressure and fiber content. When there is no confining pressure, the maximum dynamic compressive strength, dynamic elastic modulus, and modulus of deformation of SBFC4 reached 53.1 ± 2.2 MPa, 4.51 ± 0.3 GPa, and 2.55 ± 0.1 GPa, respectively, representing increases of 37.20%, 264.01%, and 59.37% compared with the control group. The dynamic compressive strength increases with the average strain rate, demonstrating a favorable strain rate effect. The energy–time history curves can be roughly divided into initial, growth, and stable stages. Under the same impact load conditions, as the fiber content gradually increases, the incident energy, dissipated energy, and energy utilization rate of the specimens all show a gradual upward trend. SEM and XRD results show that steel fibers and basalt fibers maintain good bonding with the cement matrix, contribute to the formation of gel and crystalline products within the specimens, effectively delay the initiation and propagation of cracks, and thereby improve the mechanical properties of the concrete specimens. Full article

Road transportation is a major contributor to global CO₂ emissions, yet the influence of road geometry on vehicular emissions remains insufficiently quantified under real-world conditions. This study investigates the effects of horizontal and vertical alignments on CO₂ emissions of a light-duty gasoline passenger vehicle using Portable Emissions Measurement System (PEMS) data collected along a 62.4 km highway section. Six geometric parameters longitudinal grade, cross slope, horizontal curve radius, horizontal curve length, vertical curve radius, and vertical curve length were analyzed in combination with second-by-second vehicle dynamics. The results indicate that transient CO₂ emissions exhibit substantial variability, with instantaneous emission rates exceeding 7.0 g/s under high-load conditions. Longitudinal slope gradient shows the strongest linear association with emission rate (r = 0.63), while speed and acceleration exhibit weaker but statistically significant correlations (r = 0.21 and r = 0.28, respectively). Vehicle Specific Power (VSP), representing integrated tractive power demand, demonstrates stronger association with instantaneous CO₂ emissions than individual kinematic variables. In contrast, cross slope and horizontal curvature parameters display minimal direct correlations under the tested highway conditions. A nonlinear polynomial regression model modestly improves explanatory performance relative to a linear formulation (R² = 0.21 versus 0.15; RMSE approximately 56 g/km), although a substantial portion of variability remains unexplained, reflecting the complexity of transient real-world processes. Overall, vertical alignment and transient driving conditions dominate CO₂ emission variability, while horizontal parameters play supplementary roles. These findings provide empirical evidence for refining emission models and highlight the importance of incorporating vertical alignment into sustainable roadway design and carbon reduction strategies. Full article

Existing research on driving under the influence (DUI) crashes predominantly employs independent modeling frameworks that overlook the interdependency between injury outcomes of impaired and sober drivers, potentially leading to biased parameter estimates and an incomplete understanding of crash mechanisms. This study develops a copula-based bivariate ordered response modeling framework to investigate how injury severities of DUI and non-DUI drivers are interdependent and how this dependency varies systematically across weather conditions. Using crash data from the U.S. Crash Report Sampling System (2016–2022), we analyze 3773 two-vehicle crashes involving one alcohol-impaired and one sober driver under clear, rain/snow, and fog conditions. Three key findings emerge from our analysis. First, injury severities between DUI and non-DUI drivers exhibit significant dependency, with both the strength and structure of this association varying systematically across weather conditions. Dependency intensity increases progressively from clear weather (Kendall’s τ = 0.2717) to rain/snow (0.2966) and peaks under fog (0.3239). Moreover, the optimal dependency structure differs by weather conditions. Second, DUI and non-DUI drivers demonstrate markedly differentiated response patterns to risk factors, with the same factor often producing opposite-direction or substantially different magnitude effects on the two parties. Third, weather conditions play a critical moderating role, with most risk factors exhibiting significant amplification effects on crash injury severity under adverse weather. For example, on curved roadways under fog compared to clear weather, severe/fatal injury risk increases from 4.45% to 5.81% for DUI drivers and from 7.99% to 11.36% for non-DUI drivers. These findings highlight the importance of joint dependency modeling in alcohol-related crash research and provide evidence-based insights for weather-sensitive DUI enforcement and targeted safety interventions. Full article

Pedestrian and cyclist safety at urban intersections remains a critical challenge for transportation agencies, as vulnerable road users are significantly exposed to crash risks in complex traffic environments. Identifying high-risk locations and factors that contribute to crashes is essential for improving road safety. This study developed an explainable machine learning framework to predict motor vehicle-involved pedestrian and cyclist crash occurrence at urban intersections using five years of crash, geometric, operational, and socioeconomic data from a large set of urban intersections. Five supervised machine learning algorithms were trained and evaluated, including Binary Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest. The evaluated models demonstrated strong predictive performance overall, with accuracies approaching 91% and high discriminative capability. In particular, the Binary Logistic Regression and Random Forest models achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.961 and 0.964, respectively. To enhance transparency, SHAP values were used to quantify the contribution of predictors and examine feature effects at both the global and local levels. The results indicate that roadway hierarchy, intersection markings, and total entering volume are among the most influential determinants of crash likelihood, while socioeconomic variables exhibit weaker but interpretable effects. Full article

This research examines the risk factors that influence injury severity in individual motorcycle accidents, utilising a dataset of 5253 incidents. Five machine learning algorithms—multinomial logistic regression, classification trees, random forests, XGBoost, and neural networks—were used to classify the results into three groups: Death (13.48%), Injury (80.14%), and No injury (6.38%). In all models, passenger presence was the most important predictor of injury. Motorcycle accidents involving passengers do not always have more serious consequences for several overlapping reasons. On the one hand, a motorcycle with a passenger has a significantly higher mass, which increases the braking distance and kinetic energy at the moment of collision, hindering quick defensive manoeuvres, cornering, and reactions to sudden hazards. Often, the rider also refrains from sudden movements to prevent the passenger from losing their balance. In the case of single-rider motorcycle accidents on roadways, approximately 5% of those involved with a passenger were fatalities, while approximately 48% were uninjured; in the case of those without a passenger, no one was uninjured. It follows from the above that the presence of a passenger increases the rider’s sense of responsibility. Other factors that significantly increased risk were single-lane carriageways, vehicle overturning, contaminated road surfaces, and collisions with complex objects, e.g., like trees. The multinomial logistic regression model had an overall accuracy of 69.2% on the test set. The Recurrent Neural Network achieved the best overall accuracy of 79.56%. Balanced accuracy, as the average between sensitivity and specificity of the RNN model for the “death” class was 68.15%, for the “injury” class—72.6%, and for the “no injury” class—96.61%. The Area Under the ROC Curve of the Recurrent Neural Networks model for “no injury” was 0.97, indicating it was very good at distinguishing between this class and the other classes. Even though it was easy to tell which cases did not involve injuries, it was still hard to tell the difference between fatal and non-fatal injuries in all models. The results support interventions tailored to specific situations, such as improved road lighting and speed control in rural areas, as well as helmet enforcement and safety measures at intersections in cities. Full article

To address the instability of small coal pillars in gob-side entry driving under thick and hard roof conditions, this study proposes a synergistic control technology combining “pressure relief, bundle control, and strong support”. First, a segmented deflection curve model of the coal pillar was established to quantify the correlation between pillar deformation and dominant controlling factors. Numerical simulations (FLAC3D) were then performed to optimize the roof cutting parameters, determining an optimal cutting height of 23.2 m and a cutting angle of 9°. Based on these findings, a comprehensive control scheme was implemented in the Fucun Coal Mine. Field monitoring results indicate that the proposed technology effectively controlled the lateral displacement of the coal pillar to 264 mm and maintained the stability of the roadway. This study provides a theoretical basis and practical reference for deformation control in similar geological conditions. Full article

The fire performance of existing reinforced concrete (RC) bridge decks strengthened by external prestressing systems is investigated, with particular attention to the vulnerability of externally applied tendons under realistic fire scenarios. Fire exposure represents a critical condition for such retrofitted structures, as the structural response is strongly influenced by load level, prestressing effectiveness, and thermal degradation of the strengthening system. A comprehensive assessment framework is proposed, combining thermal and mechanical analyses applied to representative highway overpass bridges. The thermal input adopted for the analyses is first validated through computational fluid dynamics (CFD) simulations, aimed at evaluating temperature development in typical RC beam–girder grillage decks subjected to fire from below. The CFD study considers variations in clearance height and span length and confirms that, in the case of hydrocarbon tanker accidents with fuel spilled on the roadway, conventional fire curves commonly adopted in the literature provide a reliable and conservative representation of both the temperature levels reached and their rate of increase within structural elements, thus supporting their use for rapid and simplified assessments. The validated thermal input is then employed in an analytical fire safety procedure applied to several realistic bridge case-studies. A parametric investigation is carried out by varying deck geometry, span length, reinforcement layout, and the presence of external prestressing retrofit, allowing the evaluation of the reduction in bending capacity and the time-dependent degradation of mechanical properties under fire exposure. The results highlight the critical role of external prestressing in fire scenarios, showing that significant loss of prestressing effectiveness may occur within the first minutes of fire, potentially leading to critical conditions even at service load levels. Finally, a multi-hazard assessment is performed by combining fire effects with pre-existing aging-related deterioration, such as reinforcement corrosion and long-term prestressing losses, demonstrating a marked increase in failure risk and, in the most severe cases, the possibility of premature collapse under dead loads. Full article

Fissured rock masses are widespread in deep underground mining engineering, and they are prone to inducing instability and failure during excavation activities. Borehole pressure relief is one of the most effective measures with which to control dynamic disaster in high-stress roadways. After pressure relief, redistribution of stress leads to stress concentration in the far-field surrounding rock (far away from working face), which can be represented by true triaxial compression state. However, current research on the energy evolution behavior of fissured rock masses under far-field conditions remains relatively limited. This study analyzes the energy evolution process, peak energy characteristics, and laws of energy storage and dissipation in fractured sandstone under different fissure dip angles (θ, 30°, 45°, 60°, 90°), with intermediate principal stresses (σ₂, 10, 20, … 120 MPa) and minimum principal stresses (σ₃, 10, 20, … 50 MPa). The results indicate that the curve of dissipated energy ratio versus maximum principal strain becomes more distinctly concave as θ increases under true triaxial compression. The growth rate of the dissipated energy ratio and dissipated energy with maximum principal strain gradually decreases when σ₃ is high, and the fissured sandstone is prone to exhibiting ductile failure, leading to a reduced energy dissipation rate. The peak elastic strain energy of fissured sandstone increases gradually with increasing σ₂ and shows a linear characteristic. The energy storage and dissipation law is nonlinear with increasing peak total energy for the fissured sandstone with different values of θ. However, the law exhibits a linear trend under varying σ₂ and σ₃. This study provides a new approach and insight into the failure characteristics of deep fissured sandstone and aims to offer theoretical guidance for the layout and construction safety of roadways or mining panels in far-field surrounding rock in future engineering practices. Full article

As a support material for mine roadways, shotcrete (SC) exhibits performance limitations in extreme deep-mining environments characterized by high stress and water seepage. Polyoxymethylene (POM) fiber, with its properties of high strength, high modulus, and corrosion resistance, holds potential for application in surrounding rock support of deep roadways. To investigate the effect of POM fiber on the flexural performance of shotcrete, four-point bending tests were conducted on fiber-reinforced concrete specimens with different fiber lengths and dosages. Combined with ABAQUS numerical simulation, damage simulation analysis was performed on each group of specimens, and the stress propagation state of the fibers was tracked. The results show that the flexural strength of polyoxymethylene fiber shotcrete (PFS) increases with the increase in fiber length and dosage, and the influence of fiber dosage is more significant. POM fiber can effectively inhibit the crack development of shotcrete, enhancing its crack resistance and residual strength. The load-deflection curves indicate that PFS exhibits excellent fracture toughness, with the P9L42 group showing the highest flexural strength improvement, reaching an increase of 94%. The numerical simulation results are in good agreement with the experimental conditions, accurately reflecting the damage state and load-deflection response of each group of concrete specimens. Based on the above research, POM fiber is more conducive to meeting the stability requirements of roadway surrounding rock support, providing a scientific basis for the application of PFS in mine roadway surrounding rock support. Full article

To address the challenge of simulating shear failure in anchor bolts within FLAC3D, a shear failure criterion, F_s(i) ≥ F_smax(i), is proposed based on the PILE structural element. Through secondary development using the FISH programming language, a modified mechanical model of the PILE element is established and integrated into the FLAC3D-FISH framework. Comparative analyses are conducted on shear tests of bolt shafts and on anchor bolt support performance under coal–rock interface slip conditions, using both the original PILE model and the modified mechanical model. The results demonstrate that the shear load–displacement curve of the modified PILE model clearly reflects shear failure characteristics, satisfying a quantitative shear failure criterion. Upon failure, both the shear force and axial force of the structural element at the failure point drop abruptly to zero, enabling effective simulation of shear failure in anchor bolts within the FLAC3D environment. Using the modified model, the distribution of principal stress differences in the surrounding rock after roadway excavation is analyzed. Based on this, the concept of a stress-bearing ring in the surrounding rock is introduced. The reinforcing effects of bolt length, spacing, and ultimate load capacity on the stress-bearing ring in weak and fractured surrounding rock are investigated. The findings reveal that: (1) shear failure initiates in bolt shafts near the coal–rock interfaces, occurring earlier near the coal–floor interface than near the coal–roof interface; (2) the stress-bearing ring in weak and fractured surrounding rock shows a discontinuous and uneven distribution. However, with support improvements—such as increasing bolt length, reducing spacing, and enhancing failure load—the surrounding rock gradually forms a continuous stress-bearing ring with more uniform thickness and stress distribution, migrating inward toward the roadway surface. Full article