In order to effectively evaluate the transportation service performance of HSRN and ensure the reliability of HSRN in the face of emergencies，based on complex network theory，a robustness evaluation method considering temporal and spatial dynamic characteristics was proposed. The dynamic changes of HSRSN was considered，and time information was incorporated into the modeling of HSRSN. Based on empirical operation data of high-speed railways in China，the necessity of considering spatiotemporal dynamic characteristics to evaluate network performance was verified，and the distribution characteristics of HSRN robustness in China were explored from the spatiotemporal dimension. The experimental results show that the train flow passing through different stations is different，and the network robustness exhibits a significant spatial distribution difference. Moreover，the disturbance scenarios are different，and the contribution of train frequency and spatial position of stations to the importance of stations is also different. In addition，the occurrence time and duration of disturbances are two key time factors that affect network robustness. Their different combinations result in significant time distribution differences in network robustness. The impact of disturbances on network performance varies at different time periods，resulting in obvious fluctuations in station importance ranking at different time periods.

In order to assist tunnel fire risk management and decision-making，the definition and principles of social risk acceptance criteria for road tunnel fire were established. Based on a statistical analysis of tunnel fire fatalities in China from 2000 to 2020 and a survey of public perceptions of tunnel fire risk，social risk acceptance criteria of tunnel fires in China was developed by the F/N curve method，and the acceptance critical limits for accident frequency and consequences were defined. The results show that the public has a tolerable attitude towards the current situation of tunnel fire accident risk. The maximum acceptable frequency of tunnel fire fatalities in China is set at 10^-5/(km·a)，and the maximum tolerable frequency is set at 10^-4/(km·a).

To explore the correlation between different characteristic factors of hydrogen accidents，827 safety accidents involving hydrogen systems were used as the data set to statistically analyze the characteristic factors such as accident date，application stage，ignition source，accident causes and consequences. First，a network of association rules for accident causation was constructed to discuss accident causation relevance. Then，the causes and consequences of the accident were analyzed interactively. Finally，Apriori algorithm-based association rule mining was performed on six types of characteristic factors of hydrogen accidents. The results show that six accident causation combinations，such as equipment failure and design error，operational error and accidental reaction，are the most highly correlated among hydrogen safety accidents. Unignited hydrogen release is strongly associated with eight factors，like pressure relief devices and ventilation systems. Hydrogen fires are strongly associated with nine factors，like bolts and spontaneous combustion. And hydrogen explosions are strongly associated with five factors，like hydrogen production，electrical sparks，and compressors.

In order to further improve the prediction accuracy of slope stability，a slope stability prediction model based on MISSA optimized SVM was proposed. Six representative indexes，including bulk density (γ)，cohesion (c)，internal friction angle (Ф)，slope angle (φ_f)，slope height (H) and pore pressure ratio (r_u) were selected as the prediction indexes of the model. In response to the problems of slow convergence speed，low accuracy，and susceptibility to local optima in the sparrow optimization algorithm (SSA)，strategies such as one-dimensional composite chaotic mapping，SCA，Levy flight mechanism，and dynamic adjustment of step size factor are introduced for optimization and improvement. A slope stability prediction model based on MISSA-SVM was constructed. The MISSA-SVM model was applied to 9 groups of slope engineering examples，such as the Daxi landslide，for verification. The results show that the accuracy，precision，recall，F₁ score，mean square error (MSE) and area under the curve (AUC) of the MISSA-SVM model reach 96.29%，92.3%，100%，0.96，0.016 and 0.967，respectively，which are better than the SSA-optimized SVM model and BP model，and the prediction results are completely consistent with the actual slope conditions，indicating that the MISSA-SVM model has strong generalization ability.

To improve the efficiency of monitoring work in the retirement treatment of uranium tailings ponds，an intelligent monitoring and prediction model of deformation of uranium tailings ponds was proposed based on SBAS-InSAR technology and BPNN. Firstly，SBAS-InSAR technology was used to obtain the cumulative deformation and annual deformation rate of the uranium tailings pond over the past two years. The accuracy of InSAR monitoring values was verified using seven Global Navigation Satellite System (GNSS) monitoring stations on the first dam. Then，the cumulative settlement of four dam sections，including Leigongtang dam，nanpo cross dam，Battle dam and Songlin dam，was selected and analyzed in conjunction with rainfall. Finally，the cumulative settlement data of 100 settlement points of the uranium tailings dam were randomly extracted to predict the deformation of the uranium tailings dam. The results show that from December 2020 to December 2022，the deformation rate of uranium tailings dam is between -60.06-34.94 mm/a. The overall settlement of the uranium tailings dam is in a sinking state，with a maximum cumulative settlement of -46.67 mm. The average absolute error between the predicted values of BPNN and the actual monitoring values is 0.586 mm，and the mean square error is 0.624 mm.

In order to prevent fire accidents in subway stations and deeply understand the internal relations and hierarchical structure among the influencing factors of fire evacuation in subway stations，63 fire evacuation accidents in subway stations were analyzed based on the sixth edition of 24Model，and the interaction among various factors was fully considered，and 19 key factors affecting fire evacuation in subway stations were extracted to establish an index system of influencing factors of fire evacuation in subway stations. C-OWA was used to improve DEMATEL to determine the important influencing factors of fire evacuation in subway stations. On this basis，ISM was used to analyze the hierarchical structure and interaction path of various factors，and a multi-level hierarchical structure model of the influencing factors of subway station fire evacuation was constructed. Research results show that evacuation guidance，panic herd behavior and crowded people are the key influencing factors for the evacuation of people in subway station fires. The evacuation of fire personnel in the subway station is influenced by surface factors，intermediate factors and deep factors. Among them，evacuation education and training，facilities maintenance and inspection，evacuation plan and other factors are the root causes. Paying attention to the improvement of root causes was conducive to preventing and controlling accidents in essence.

In order to solve the problems of lack of timeliness in the evaluation indicators of urban rail transit operation safety and some safety evaluation models ignoring the fuzzy boundaries of evaluation levels，a new evaluation index system for the operation safety of urban rail transit lines was reconstructed，including new indicators such as the implementation of infectious disease prevention and control measures by employees，the degree of cooperation between relevant units，and the completeness of prevention and control equipment. Secondly，the combination weighting of various indicators was carried out using the Best Worst Method (BWM) based on the minimization of dispersion and the anti-entropy weighting method. Then，the extension cloud was applied to determine the safety evaluation level of urban rail transit line operation. Finally，take Shanghai Metro Line 17 as an example for analysis. The results indicate that the comprehensive cloud correlation degrees of various indicators for the operational safety assessment of Shanghai Metro Line 17 are below 0.5，with a confidence level exceeding 0.99，among which the confidence levels for personnel factors，line factors，and management factors are all 1. This suggests that the employed indicator system and evaluation model are closely aligned with the current operational conditions of urban rail transit，accurately reflecting the safety levels of urban rail transit operations. Further analysis of the sensitivity of indicator values reveals that the reliability of station operational equipment has the most significant impact on the actual operational safety of the line. In practical operations，the degree of passenger cooperation in preventing and controlling infectious diseases at stations has the greatest potential to improve the indicator. It can help subway companies establish a more scientific and effective safety evaluation system to ensure the safety of daily line operations.

In order to solve the problem of secondary disaster risk in coal mines affected by earthquake disasters，firstly，typical cases have been investigated and analyzed，and the types and damage characteristics of secondary disasters in coal mines have been summarized. Based on the comprehensive elements of "man-machine-environment-management"，a three-level indicator system for earthquake-induced secondary disaster risk assessment in coal mines has been constructed. Then，the evaluation method of risk level and importance of hidden danger has been proposed. Finally，a coal mine in a city in Hebei Province was used as an example for practical application. The results show that the types of secondary disasters induced by earthquakes in coal mines can be divided into 6 categories according to the disaster-bearing body. The assessment of the possibility of earthquake-induced coal mine secondary disasters mainly considers six aspects: the earthquake disaster level in the area where the coal mine is located，the risk of coal mine secondary disasters，the hazard of coal seam gas occurrence，the hazard of hydrogeological conditions in the mine，the level of fortification of the disaster-bearing body，and the ability to prevent and mitigate disasters. The assessment of the severity of the consequences mainly considers two aspects: the number of casualties and economic losses that may be caused by disasters. The weak links in seismic fortification of coal mines in this city are ventilation system，shaft support system，and drainage system，which should be targeted to improve seismic fortification level.

To quantitatively evaluate the influence of snowfall on the resilience of airport infrastructure system，the airport infrastructure system was divided into four first-level indicators，including physical system，functional system，economic system and organizational system. The damage intensity coefficient of snowfall weather to the second-level indicators was put forward，and a quantitative analysis model of airport infrastructure system resilience was established. The variation of resilience level of airport infrastructure system was simulated by the Monte Carlo Method under four different levels of snowfall. The simulation result show that the functional damage degree and recovery time of the airport infrastructure system are positively correlated with snowfall intensity. The system recovery time is 13，14，16 and 23 respectively under the conditions of light snow，moderate snow，heavy snow and blizzard. The resilience of the airport infrastructure system under the condition of blizzard is 9.65%，17.01% and 20.27% lower than that under the conditions of heavy，medium and light snow，respectively. The recovery speed of the airport infrastructure system increases from 0.053%/h to 0.061%/h，and the recovery time reduces from 12 h to 9 h when the failure strength coefficient reduces by 20%.

To address the limitations of evaluating the emergency response capability of port ships for oil spills in a single stage and reasonably allocate resources，the cloud gravity center evaluation method was adopted to evaluate the comprehensive emergency response capability of port ships for oil spill. Facing the entire process of emergency preparedness，early warning，response and recovery，and referring to the theory of "man-machine-environment-management" system and international and domestic regulations，a comprehensive emergency response capability evaluation index system was established. To address the issue that the traditional evaluation indicators were not fuzzy and random，a weighted deviation degree was used to determine the transformation relationship between qualitative indicators and quantitative values. AHP was used to optimize evaluation opinions and compensate for accuracy deficiencies caused by insufficient evaluation samples. Finally，taking Shanghai Port as an example，an empirical study was conducted on the comprehensive emergency response capability evaluation of ship oil spills. The results show that the cloud gravity center evaluation method can reasonably evaluate the comprehensive emergency response capability of port ships for oil spills. This method provides a theoretical basis for improving and perfecting emergency response capability，and provides a new approach for the comprehensive emergency response capability evaluation of port ships for oil spills.