To manage risk transmission and reduce the probability of construction safety accidents, the evolution process and characteristics of construction safety risk transmission in high-altitude areas were explored. Firstly, based on construction accident reports of hydropower projects in high-altitude areas and the 4M1E theory, a construction safety risk factor system was constructed from five aspects: personnel, machinery and equipment, materials, management, technology, and environment. The Decision-Making Experiment and Assessment Technique (DEMATEL) was used to analyze risk events triggered by risk factors. Then, based on the SFEP theory, a construction safety risk transmission network was established, and the risk transmission probability of each path was calculated through association rules. The SD method was utilized to construct an SD model of the construction safety risk transmission network. Finally, a large hydropower project in Xizang was taken as an example for simulation verification. The results show that the SD model of the construction safety risk transmission network reveals 29 risk transmission paths from 17 edge events, 15 process events, to 5 final events, as well as their evolution trends and sensitivities. Environmental and management risk events are identified as key nodes, and three key transmission paths are identified. Based on this, targeted risk transmission prevention and control measures are proposed, providing a theoretical basis for the management of construction safety risks in hydropower projects in high-altitude areas.

To fundamentally enhance the safety of lithium-ion batteries, phosphorus-based flame retardants were added to the electrolyte, and the effects of different valence states of phosphorus-based flame retardants on safety performance and electrochemical performance were studied. The impact of phosphorus-based flame retardants on the thermal stability of the electrolyte was compared using self-extinguishing time and differential scanning calorimetry. The influence of phosphorus-based flame retardants on the basic properties of the electrolyte was analyzed through linear sweep voltammetry. Further, cyclic voltammetry tests, cycling performance tests, rate capability tests, and electrochemical impedance spectroscopy were conducted to explore the effects of varying volume ratios of phosphorus-based flame retardants on the electrochemical performance of LiFePO₄|Li half-cells. The results show that electrolytes containing 5% trimethyl phosphate (TMP) and trimethyl phosphite (TMPi) exhibit significantly reduced self-extinguishing times, with the former also expanding the electrochemical window of the electrolyte. The LiFePO₄|Li half-cell with 5% TMP demonstrates a smaller potential difference between the oxidation and reduction peaks, reduced battery polarization, and maintains a higher discharge capacity after 300 cycles, with a capacity retention rate of 99.6%. In contrast, the addition of 5% TMPi leads to a decline in discharge capacity. Methyl phosphate flame retardants exhibit weaker molecular activity and less adverse effects compared to methyl phosphite flame retardants. Under the premise of not compromising electrochemical performance, adding 5% trimethyl phosphate is more effective in improving the safety of lithium-ion batteries.

To address the issues of low accuracy and weak robustness in existing detection algorithms due to insufficient lighting, scale differences among personnel, and frequent obstruction by equipment in coal mine environments, as well as the challenges posed by high parameter and computational requirements of some models, which make them difficult to adapt to edge devices underground, an improved YOLOv8n model was proposed to optimize personnel detection tasks in complex mine environments. An enhanced SPDs-Conv module was introduced to enhance the extraction of small target features and improve the recognition accuracy of low-pixel personnel in distant views. Cross stage partial feature fusion + selective kernel attention (C2f_SKAttention) module was designed to strengthen the model's focus on targets of different scales and cope with the scale differences of underground personnel. A dynamic detection head was constructed to adapt to the diversity and complexity of targets, and to improve robustness to occlusion and other scenarios. The WIoU loss function was improved to increase the bounding box localization accuracy and reduce the localization deviation caused by low illumination. The results show that the proposed improved YOLOv8n model achieves an mean average precision (mAP) @0.5 of 83.5% and an mAP@0.5:0.95 of 39.0% on the mine personnel detection dataset. Compared with the original YOLOv8n, the P is improved by 8.5%, the R by 11.9%, the mAP@0.5 by 4.7%, and the mAP@0.5:0.95 by 3.3%. The number of parameters only increases from 3.1M to 3.2M, and the Giga Floating-point operations per second (GFLOPS) rises from 14.0G to 14.4G. The proposed model maintains a lightweight structure while improving detection accuracy and robustness. It effectively alleviates missed detection of small underground targets, insufficient multi-scale adaptation and weak anti-interference capability in complex environments, making it suitable for the limited computing power of underground edge equipment.

To promote the engineering application of VIMD in high-rise base-isolated structures, a 20-story base-isolated steel structure model was considered. First, the influence of the VIMD's inertance on the structure's natural periods was investigated. Then, 10 real ground motion records were selected from the Pacific Earthquake Engineering Research Center database and were scaled to match the target design response spectrum. The control characteristics and seismic mitigation effects of VIMD on the structural response were studied. Finally, based on the Davenport fluctuating wind speed spectrum, the spectral representation method was used to generate 10 stochastic fluctuating wind speed time histories. The control characteristics and wind-induced vibration mitigation effects of VIMD on the structural response were studied. The results indicate that VIMD can further extend the natural periods of the high-rise base-isolated structure, mainly in the first six modes. Under seismic excitations, the control effectiveness of VIMD on the relative displacement response of the isolation layer is comparable to that of Viscous Dampers (VD). However, the vibration mitigation ratios of VIMD for the response of the superstructure is improved by 19.5%-24.5% compared with that of VD. Under wind loads, VIMD and VD have basically the same response control capabilities for the high-rise base-isolated structure.

To study the influence of initial temperature on the combustion behavior of pool fires, pool fire tests at different initial temperatures (5, 10, 20, 40, 60 and 80 ℃ ) were conducted using a self-built initial temperature controlled pool fire test platform. The characteristics of variations in combustion process, mass loss rate, flame height, and plume temperature were analyzed. The results show that when the initial temperature ranges from 5 to 60 ℃, the combustion process of oil pool fire is divided into three stages: growth, steady and decay. When the initial temperature increases to 80 ℃, the combustion process is divided into five stages: growth, steady, boiling transition, boiling and decay. The mass loss rate, flame height and plume temperature are all positively correlated with the initial temperature. When the initial temperature increases from 5 ℃ to 80 ℃, the mass loss rate, flame height and plume temperature increased by 12.13 g/(s·m²), 170.4 mm, and 130 ℃, respectively. The mass loss rate decreases nonlinearly with the temperature difference between the boiling point of n-heptane and the initial temperature. The ratio of flame height to pool diameter follows a power-law function of the ratio between the initial temperature and the boiling point of n-heptane point.

To improve the detection efficiency and automation level of hidden defects on the outer surface of elevator traction steel wire ropes, and reduce the incidence of elevator accidents, an online detection model for defects on the outer surface of elevator traction steel wire ropes based on improved YOLOv5s is constructed. Firstly, the GhostConv module is introduced into the feature extraction layer C3 module to reduce computational complexity, and the Convolutional Block Attention Module (CBAM) is integrated to enhance the feature extraction capability of small-scale defects. A feature extraction module GC-C3 (GhostConv and CBAM-C3) that integrates GhostConv and CBAM is constructed; Secondly, in the feature fusion layer, Path Aggregation Network (PANet) and Bidirectional Feature Pyramid Network (BiFPN) are used to construct a multi-scale feature fusion network PBNet (PANet BiFPN), which combines multi-scale weight allocation strategy to improve the fusion effect of multi-scale defect feature information; Then, dynamically adjusting the quality weights of prediction boxes using Weighted Intersection over Union(WIoU) loss function, reducing the interference of low-quality samples on training. Finally, the model will be deployed to the developed detection system to perform online testing on the surface defects of the traction steel wire rope on the elevator car roof, verify the improvement effect of the model, and provide grading reminders for the defects. The results showed that the average detection accuracy of the improved model was 96.2%, with a detection speed of 192 f/s, which was 4.1% and 12.3% higher than the original model, respectively. The model volume was reduced by 38.9%. According to the online visualization experiment, under the actual operating environment of the traction steel wire rope (light illumination of 200~400 lx, speed of 1.5~2 m/s), the average accuracy of the system for 8 typical external surface defects is still stable at 94.6% or above, which meets the application requirements of online detection of hidden dangers of external surface defects of the traction steel wire rope in service and reduces elevator accidents caused by external surface defects of the traction steel wire rope.

In order to elucidate the combustion and explosion of volatile gas leaks caused by degradation of the floating roof seal performance and its destructive mechanism on composite floating roofs, a multiphase coupled model of liquid storage-composite floating roof incorporating the cell structure of the honeycomb core layer was established. Multiphase coupling analysis of the tank's combustible gas, composite floating roof, and stored liquid was employed in this study to compare damage patterns in the roof panel and honeycomb core under implosion loads. A methodical inquiry was initiated to explore the impact of panel layering angles, honeycomb geometric parameters (including wall thickness, height, and edge length), and cellular element configurations (i.e., regular hexagons, circular, close-packed, and sparingly packed cells) on the blast resilience performance of floating roofs. The results indicate that, in circumstances where liquid levels are at a low ebb, the upper panel is primarily subject to matrix tensile damage (6.82% area fraction), accompanied by 0.16% fibre compression and matrix compression damage. The optimal panel lay-up angle [45°/90°/45°/90°] has been demonstrated to reduce matrix tensile damage to 5.03% area fraction, thus yielding the optimum level of explosion resistance. Hexagonal honeycomb cores have been shown to demonstrate superior blast resistance in comparison to circular cores, while densely packed circular honeycomb exhibits greater load-bearing capacity than sparsely packed configurations. Increasing the thickness and height of honeycomb cells, or reducing cell edge length, has been demonstrated to enhance the floating roof's capacity for blast resistance.

In order to reduce accident risks caused by leakage during the refueling process, the hydrogen leakage and diffusion behavior of a 35 MPa hydrogen dispenser was numerically simulated using Ansys Fluent. The characteristics of hydrogen leakage and diffusion under the canopy structure in the refueling zone were investigated. The effects of leakage diameters, ambient wind velocity, and local ventilation on hydrogen concentration distribution and the evolution of flammable areas were analyzed. The results show that when the leakage diameter of filling hose is 2 mm, no flammable area is formed on the underside of canopy. However, when the leakage diameter increases to 5 mm and 10 mm, a flammable area can develop on canopy underside. The location of the highest hydrogen concentration on underside of canopy is concentrated near the axis parallel to the jet direction. Specifically, when leakage diameter is 10 mm, the hydrogen concentration on underside of canopy along vertical leakage direction exhibits a Gaussian distribution. When the ambient wind is perpendicular to leakage direction, wind velocities of 2 m/s and 8 m/s can effectively reduce hydrogen accumulation near the leakage hydrogen dispenser. In contrast, at the wind speed of 5 m/s, a vortex structure was formed near obstacles, leading to hydrogen accumulation and increasing the risk of fire and explosion. Under no ambient wind conditions, local ventilation is provided in the refueling zone. When the ventilation velocity reaches 5 m/s and 10 m/s, the hydrogen cloud concentration within the flow field can be successfully diluted to below the flammable limit within 2 s. Moreover, a ventilation velocity of 10 m/s shows a more pronounced effect in reducing the hydrogen concentrations in front of the leakage source.

To investigate the effects of solar irradiation and wetting-drying cycles on the crack evolution of compacted loess and clarify the underlying mechanisms, laboratory tests were conducted using a xenon lamp to simulate solar irradiation under varying irradiance levels, dry densities, and wetting-drying cycles. Surface crack images were periodically captured using a self-developed acquisition system. Crack morphological parameters were extracted using the Particle and Crack Analysis System (PCAS), and micro-pore structures were quantitatively analyzed based on scanning electron microscopy (SEM) images, enabling a systematic macro-micro analysis of crack evolution characteristics. Results indicate that increasing irradiance accelerates crack initiation and increases crack ratio, main crack length, and overall fractal dimension. Within the dry density range of 1.5-1.7 g/cm³, higher dry density effectively reduces crack ratio and connectivity, thereby inhibiting crack propagation. Under wetting-drying cycles, porosity generally increases, pore circularity decreases, and fractal dimension shows an initial increase followed by fluctuations, corresponding well with macroscopic crack evolution. Solar irradiation enhances surface evaporation, intensifies moisture migration and deformation heterogeneity, and promotes the transition from pore structure adjustment to macroscopic crack propagation.

To address the problems of low accuracy and insufficient adaptability in existing methods for determining the parameters of PIM for predicting surface deformation prediction in goaf areas under thick unconsolidated layers, 36 sets of measured surface movement data from coal mining working faces were selected. The core indicators of mining-geological conditions were screened via Hierarchical Cluster Analysis (HCA), Entropy Weight Method(EWM) and Grey Relational Degree (GRD) analysis. Furthermore, the GRNN model was optimized by integrating K-fold cross-validation with the neighborhood perturbation strategy of SAA, and an SAA-GRNN optimization model was constructed for PIM parameter determination. A case study was conducted using 45 sets of data from coal mining working faces with thick unconsolidated layers in the Jining area. The results show that: seven mining-geological condition indicators can be classified into three categories, and five core input indicators were identified screening, namely mining thickness M, coal seam dip angle α, mining depth H, strike mining degree D₃/H, and unconsolidated layer thickness h. The maximum root-mean-squared error (RMSE) of SAA-GRNN model is no more than 0.190 4, the maximum mean absolute error (MAE) is controlled within 0.133 9, the maximum mean absolute percentage error (MAPE) is 0.153 6, and the overall coefficient of determination (R²) is generally above 0.8. Under the same conditions, the prediction errors are greatly reduced compared with those obtained using Back Propagation (BP) neural network and the conventional GRNN model.