Warming and wetting on the Qinghai-Tibet Plateau (QTP), together with increasing engineering disturbance, have accelerated permafrost degradation along the Qinghai-Tibet Engineering Corridor (QTEC), inducing surface deformation that threatens infrastructure stability and environmental safety. Interferometric synthetic aperture radar (InSAR) has become an effective tool for large-scale deformation monitoring; however, conventional single-orbit ascending or descending observations measure only one-dimensional line-of-sight (LOS) displacement. Because LOS deformation represents a projection of actual ground motion, it cannot always be directly interpreted as vertical uplift or subsidence, especially where horizontal displacement is significant. In this study, ascending and descending Sentinel-1 InSAR observations from 25 February 2017 to 31 March 2022 were integrated to decompose two-dimensional deformation components, namely east-west and vertical displacement, in the permafrost zone of the QTEC. Based on the decomposed results, surface-deformation types were discriminated, and the interpretation applicability of conventional single-orbit LOS observations was quantitatively evaluated using vertical deformation as a reference. The results show that single-orbit LOS deformation can reasonably represent vertical deformation in 52.84 % of the study area, whereas the remaining 47.16 % is dominated by horizontal displacement and is therefore unsuitable for direct interpretation as uplift or subsidence. These findings highlight the necessity of distinguishing deformation types before interpreting LOS deformation and provide practical guidance for improving the reliability of InSAR-based deformation monitoring and engineering-risk assessment in permafrost corridors.
Steep, highly fractured rock slopes, characterized by lithological heterogeneity, intense fracturing, and sensitivity to rainfall-induced hydraulic perturbations, pose challenges that are not covered by classical sliding-surface models. This study integrates multiphysics monitoring, laboratory experiments, and probabilistic inversion to investigate a representative slope at the Luanchuan Longyu open-pit mine. A positive feedback loop was found between seepage and fracture development: elevated pore pressure induced by rainfall or increased groundwater levels drives fracture propagation, enhances permeability and seepage velocity, reduces effective stress, and initiates a self-accelerating “seepage → fracture → rock weakening” chain, even in the absence of a continuous slip surface. Lithological analysis reveals that marble and gneissic granite exhibit high strength (UCS ≈ 57-58 MPa, c ≈ 6.2-6.8 MPa, ϕ ≈ 39°-43°), whereas quartz-mica schist is a highly weathering-sensitive weak layer (saturated UCS = 9.78 MPa, with 35 % strength loss and SDI = 58.96 % after 11 wetting-drying cycles). Parameter back-analysis (with 80 % confidence intervals) reveals schist ( c = 24.5 kPa, ϕ = 34.4°) as the dominant weak stratum and faults ( c = 8.9 kPa) as potential slip surfaces, clarifying the lithology-structure controls on failure patterns. Rainfall infiltration triggers a nonlinear hydraulic response and preferential toe flow, causing pore pressure to propagate downward and shifting slope degradation from surficial to whole-slope failure-a dynamic "surface-to-depth" progression modeled in this study. These findings provide a quantitative mechanistic framework for assessing steep, heterogeneous slopes and support intelligent hazard mitigation through rainfall thresholds and real-time monitoring.
Concrete wall hollowing is a common defect affecting structural safety and durability, and its accurate identification is of great significance for ensuring the quality of construction projects. To achieve efficient and non-destructive detection of hollowing defects, this paper proposes a method for identifying the state of concrete wall hollowing using the characteristics of knock sound signals based on the principle of acoustic response. The study first collects acoustic signal data from hollowing and non-hollowing areas through standardized knocking experiments, and conducts systematic time-domain and frequency-domain feature analysis. By comparing signal waveform and spectral characteristics, the study explores the differences in acoustic response between hollowing and non-hollowing areas. The research results provide a reliable parameter basis for acoustic detection of concrete structures and verify the feasibility and effectiveness of the hollowing identification approach based on acoustic signal characteristics. This research method has the advantages of simplicity, low cost, and strong applicability, and can provide new technical support and application references for non-destructive testing and intelligent health monitoring of building structures. The results show that under the experimental conditions of this study, the dominant frequencies of hollow areas are mainly distributed in the range of 1650-1750 Hz, while those of non-hollow areas are concentrated within 1300-1500 Hz. The absolute values of peak amplitudes of hollow areas generally fall between 40-50 mV, whereas those of intact regions range from 10-20 mV, which provides quantitative reference criteria for hollow defect detection in practical engineering.
As key equipment in the power system, the operating state of switchgear circuit breakers directly affects power grid reliability. Traditional fault diagnosis methods suffer from complex installation and high susceptibility to interference, while voiceprint diagnosis has become a research hotspot due to its non-contact, high sensitivity and easy deployment. However, the acquisition orientation (angle and distance) of sensors can significantly change voiceprint signal characteristics and reduce diagnostic accuracy. Therefore, this paper takes 10 kV switchgear circuit breakers as the research object, collects opening and closing voiceprint signals at different angles and distances through experiments, and studies the influence of sensor orientation on voiceprint characteristics. Time-frequency analysis shows that the energy of the closing signal is about twice that of the opening signal. Within the range of ±45°, the angle change has little effect on the energy (difference < 8 %), and the 0° orientation has the best signal-to-noise ratio. The angle has no significant effect on the frequency-domain characteristics, but the closing signal has richer high-frequency components. Distance experiments show that the signal energy follows the 1 / r 2 attenuation law, and the high-frequency components attenuate faster. Accordingly, this paper proposes a signal compensation method based on energy normalization, which can eliminate the spectrum distortion caused by distance and keep the signal characteristics consistent at different distances. The results provide a theoretical basis for the optimal layout of sensors in circuit breaker voiceprint diagnosis. The proposed compensation method can effectively improve the accuracy of fault diagnosis models and has important engineering application value for power equipment condition monitoring.
This paper addresses the inefficient and error-prone identification of tree hazards near transmission corridors by proposing a recognition method that uses 3D R-tree-integrated point cloud segmentation. LiDAR-equipped UAVs capture the original point cloud data of the corridor. The data are denoised and enhanced using principal component analysis (PCA). A 3D R-tree-integrated octree indexing structure is constructed to rapidly locate potential tree hazards by querying regions within the minimum safe distance. A Euclidean clustering algorithm with cylinder k-point constraints is applied to extract these hazardous point clouds. The spatial location of the transmission corridor is determined by fitting its point cloud via RANSAC-based least squares. Finally, distances between tree crowns and the corridor are calculated to identify hazards. Experiments demonstrate that the proposed method is efficient, accurate, and sensitive, offering an intelligent solution for automated corridor monitoring.
