In order to reduce the risk of traffic accidents in expressway agglomerate fog environment, the expressway agglomerate fog zone was divided into transition zone and conflict zone, and driving simulation tests were designed and carried out. Firstly, Analysis of Variance(ANOVA)and non-parametric tests were used to analyze the driving behavior in the two zones. Then, Pearson correlation analysis was used to explore the correlation characteristics of the driving behavior indicators between transition zone and conflict zone. Finally, a binary logistic regression model was used to explore the mechanism of the influence of driving behavior on the collision risk. The study shows that: in transition zone, driving experience and visibility change rate have a significant effect on speed standard deviation and inner boundary speed. Compared with novice drivers, experienced drivers have 36.8% lower speed standard deviation and 13.5% higher inner boundary speed. Visibility change rate has a statistically significant difference on speed standard deviation and inner boundary speed. Different visibility change rates correspond to different inner boundary speed and speed standard deviation. In conflict zone, time to collision (TTC) was significantly positively correlated with visibility, and reaction time was significantly negatively correlated with visibility. TTC, reaction time and collision rate were all significantly negatively correlated with the conflict distance. The speed adjustment behavior in the transition zone significantly affects collision risk in conflict zone.

In order to investigate and predict the internal failure and instability characteristics of gas hydrate coal samples under triaxial action, laboratory triaxial compression tests were first conducted based on the gas hydrate solidification method. Using scanning electron microscope(SEM) images as reference, composite Clump particle templates were constructed to establish biaxial compression models through discrete element method (DEM). The models were featured different particle shapes (irregular and spherical) and saturation levels (40%, 60%, 80%), followed by simulated triaxial compression tests. A comparative analysis was then performed from both macroscopic and mesoscopic perspectives to examine the effects of Clump particles versus spherical particles on the mechanical behavior of gas hydrate-bearing coal. The results demonstrate that the peak strength of specimens increases with hydrate saturation for both particle shapes. The enhancement becomes less pronounced at higher saturation levels. Irregular particle specimens exhibit significantly greater strength than spherical ones, with maximum strength improvement reaching 142.01%. Both saturation and particle shape show limited influence on failure modes. All specimens exhibit bulging failure and X-shaped shear band distribution. Spherical particle specimens display substantially more particle rotations and larger failure zones compared to irregular particles. During loading, irregular particles form stable load-bearing networks through interlocking, supported by their notably higher average coordination number than spherical particles. These findings confirm the superior performance of irregular particle models in characterizing the mechanical behavior of gas hydrate-bearing coal.

In order to evaluate the effectiveness of aerogel fire suppressants in preventing and controlling thermal runaway of lithium-ion batteries, this study selected aluminum-shell ternary lithium-ion battery cells and 18650 battery modules as experimental subjects. By employing differentiated fire suppression approaches (including application methods and operational parameters), comparative experiments on extinguishing efficacy were conducted in a systematic manner, with parallel comparisons to water-based fire suppression systems. The results show that the aerogel extinguishing agent has better fire extinguishing performance and cooling effect than water for different quantities of ternary aluminum-shell single lithium batteries and 18650 battery modules. For four ternary aluminum shell single lithium batteries, using water to extinguish fire will cause the battery temperature to rise to 492 ℃, which cannot quickly and effectively reduce battery temperature. The extinguishing speed of an aerogel extinguishing agent is 4.5 times that of water, and it can effectively prevent the battery from reburning. For 8 ternary aluminum shell single lithium batteries and two 18650 battery modules, the aerogel extinguishing agent still shows a good fire extinguishing effect, short fire extinguishing time, significant cooling effect and no reburning. Use of the aerogel extinguishing agent and fire extinguishing strategies of continuous spraying with large flow, small flow cooling after large flow spraying and spraying and flooding in containers have different fire extinguishing effects. Results show that the aerogel extinguishing agent can not only effectively extinguish initial battery fire, but also its preheating foaming mechanism and good foam stability can effectively reduce battery temperature and prevent further mixing of combustible gas and air.

In order to accurately locate the hidden spontaneous combustion area inside the broken coal pillar using elastic wave detection, the response characteristics and mechanism of the elastic wave velocity of coal to temperature were experimentally studied. Firstly, coal samples were pretreated by temperature programmed furnace to obtain coal samples with different oxidation degrees. Then, elastic wave and low-field NMR tests were conducted to analyze the characteristics of elastic wave and pore cracks of coal with different oxidation degrees. Finally, combined with thermogravimetric(TG) analysis and single variable briquette experiments, the elastic wave response characteristics and mechanism of coal oxidation spontaneous combustion were revealed. The results show that the P-wave and S-wave velocities of raw coal gradually decrease with increasing temperature, and the P-wave velocity decreases 2.4 times that of the S-wave velocity after the maximum mass loss temperature of TG curve. The decreasing trend of P-wave velocity shows an obvious segmental characteristic. Before the maximum mass loss temperature of the TG curve in stage Ⅰ, the P-wave velocity decreases at a rate of approximately 1.27 (m·s^-1)/℃. from the maximum mass loss temperature to the final temperature, the decline rate of P-wave velocity increases to 4.51 (m·s^-1)/℃. The porosity of coal increases from 1.57% at 30 ℃ to 9.6% at 360 ℃. In particular, after the temperature exceeds the maximum mass loss temperature, the number and aperture of cracks increase significantly, which not only lengthens the propagation path of elastic waves, but also decreases the propagation speed of elastic waves. As the propagation medium of elastic waves, the coal matrix does not decompose obviously before the maximum mass loss temperature, and the corresponding P-wave velocity of briquette remains stable at about 900 m/s. After exceeding the maximum mass loss temperature, the coal matrix decomposes rapidly, and the corresponding P-wave velocity of briquette drops to 798 m/s at 360 ℃, which proves that the coal matrix gradually transitions from a "high wave velocity" medium to a"low wave velocity" medium.

In order to solve the problem of mixed heavy metal substances in coal hindering the infiltration of dust suppression solution into coal pores and cracks, resulting in a weakened wetting effect of coal, experiments were conducted to explore the synergistic effect of chelating agents and surfactants on coal wettability. Firstly, based on the water injection test platform and the dust cutting simulation test platform, six injection methods were designed to experimentally determine the antagonistic effect between surfactants and chelating agents. Then, the effects of six injection methods on the dust production characteristics and wetting effect of coal samples were studied through cutting tests and surface wettability analysis. Finally, spray dust suppression was conducted to test the settling efficiency of pulverized coal enhanced by different injection methods. The results show that the coal sample treated by first injecting the chelating agent followed by the surfactant exhibits the lowest dust production concentration, at only 170.93 mg/m³, which is 64.49% lower than the 481.33 mg/m³ in the pure water group. Furthermore, the sequential injection protocol employing prior a chelating agent infusion followed by a surfactant delivery exhibits the most significant improvement in coal wettability, with the fastest rate of wetting-induced sedimentation of coal dust. In addition, the coal samples treated by the above method achieve a dust suppression efficiency of 91.2% in in spray dust suppression tests. Through molecular simulation and theoretical analysis, injecting a chelating agent solution to decompose heavy metals in coal fractures can improve the fluidity of the solution in coal. Subsequently, injecting a surfactant solution further enhances the wettability of coal.

To reduce traffic accident risks，on-road experiments were conducted to investigate the differences in potential risk perception ability between skilled and unskilled drivers under two typical risk scenarios：dynamic motorcycle-following and parallel overtaking. A wearable eye tracker was employed to collect drivers' dynamic visual parameters，with their visual characteristics analyzed across different scenarios. The results demonstrate that skilled drivers exhibit significantly stronger risk perception abilities than unskilled drivers in both scenarios. Specifically，in the dynamic motorcycle-following scenario，skilled drivers show a higher probability of fixating on distant areas ahead，enabling better prediction of upcoming traffic conditions. In the parallel overtaking scenario，skilled drivers display shorter fixation durations，along with greater horizontal search breadth and vertical search depth. Moreover，the proportion of fixation time on rearview mirrors is significantly higher for skilled drivers compared to unskilled drivers，indicating superior visual search efficiency and enhanced rear traffic monitoring capability. Evaluations using the grey near-optimal comprehensive evaluation method reveal that skilled drivers achieved significantly higher scores in hazard perception ability.

To address the issue of natural coal ignition in goaf under normal fault geological structures，the oxygen consumption rate and heat release intensity of coal samples were measured using a temperature-programmed oxidation device. Based on a porous media model of the goaf and the gas component transport equation，a numerical model for CO₂ injection via side pressure into the goaf influenced by normal faults was established. The numerical model was used to simulate the mechanism by which the variation in the distance between the working face and the fault affects the width of spontaneous combustion oxidation band in the goaf，and analyze gas migration characteristics under different CO₂ injection locations and flow rates. The results indicate that as the distance between the working face and the fault increases，the width of the oxidation band initially increases and then decreases，reaching a maximum width at 70 m from the fault. With the increase of CO₂ injection depth，the oxidation band width initially decreases and then increases again，reaching a minimum width when the CO₂ injection position is 40 m from the working face. Furthermore，with the increase of CO₂ injection volume，the width of the oxidation band width decreases following a negative exponential trend. When the CO₂ injection rate is 1000m³/h and CO₂ volume fraction at the working face is below 0.4% for safety，width of the oxidation band reaches its minimum.

In order to reduce the various risks associated with storm and flood hazards，an ontology model of storm and flood hazard risk is proposed. Storm and flood disaster risk elements were identified from five aspects：environmental factors，equipment and facility factors，management factors，human factors，and information factors. The concepts of storm flood class and state space were given respectively，and data attributes and object attributes of each hazard element are defined. And visualization of the storm flood ontology model based on Protégé application. The results show that the storm and flood ontology model can express the risk elements and attributes clearly and accurately，and retrieve the links between the risk elements scientifically and quickly，so as to help the decision-makers respond effectively. Providing a data storage and retrieval platform for heavy rainfall and flood disaster risk assessment，it can realize user knowledge reuse and sharing，and provide a reference for emergency response and scientific decision-making by the government and emergency management authorities at the same time.

To gain a deep understanding and evaluate the command capability and current status of counties in response to flood disasters，this paper identified 12 major influencing factors through literature analysis. Social network analysis was conducted using Ucinet software，and Netdraw software was utilized for visualization to depict the social network relationship diagram among these influencing factors. By calculating the in-degree and out-degree，as well as centrality of the influencing factors，indicator classification was performed. Drawing on the maturity model of command and control capability，an assessment model based on social networks for the maturity of county-level flood disaster emergency command capability was developed. Puyang county was selected as the empirical research object. Through questionnaires and interviews，a total of 342 data entries from 24 townships in Puyang County were collected to evaluate the county's flood disaster emergency command capability. The results indicate that it is feasible to classify indicators hierarchically through social network analysis. The established indicator system is scientifically and accurately reflected in the comprehensive operational level of county-level flood disaster emergency command. The assessment model can effectively evaluate the maturity levels of emergency command capability.

In order to solve the problems that machine learning exists in the hazard intelligent prediction field of tunnel water inrush，such as relatively simple models and imperfect prediction accuracy，a prediction model based on the stacking ensemble learning was proposed. Firstly，the tunnel water inrush disaster dataset was established by collecting 232 groups of water inrush disaster data from 95 tunnels，and the data was preprocessed. Then，3 base learners and 2 meta learners were selected to train 8 sets of stacking ensemble models in different combinations，and 6 sets of optimal ensemble models were selected. Finally，the optimal stacking ensemble model was selected by comparing and analyzing the prediction results of 6 groups of parameters optimized and stacking ensemble model with the grid search parameters and the 5-fold cross-validation hyperparameter optimization model. The results show that SVM(Support Vector Machine )+NB (Naive Bayes) + LR (Linear Regression) ensemble model is obtained after the optimal single model SVM is improved with the stacking ensemble learning method. Its accuracy，recall，and F₁ score are 0.94，0.91，and 0.92，respectively. The overall prediction effect is better than that of other compative models，and it can accurately predict the hazard level of tunnel water inrush.