The weak permeability of fine-grained tailings can cause the leaching line of the tailings dam to rise and reduce the safety factor of the tailings dam，an ultrasonic cavitation approach was proposed to enhance the permeability of the tailings. Firstly，computational fluid dynamics (CFD) software was used to simulate the cavitation bubble collapse process. Then，the transducer used for the experiments was selected based on the cavitation threshold and the simulated sound pressure，with a frequency range of 20-40 kHz and a power of 60 W. Moreover，the presence of ultrasonic cavitation effects in the tailings samples was confirmed by staining tests. Finally，ultrasound-enhanced permeability tests were performed on tailings samples with different fine particle contents using a self-made variable head permeameter and the selected transducer. A nuclear magnetic resonance instrument was used to determine the pore structure changes before and after penetration enhancement. The results showed that the obtained cavitation bubble radius change curve was consistent well with the Rayleigh-Plesset(R-P) equation fitting curve，proving the validations of the simulations. Low-frequency ultrasound had a better cavitation effect when the bubble diameter was larger than 50 μm，whereas high-frequency ultrasound was more effective when the bubble diameter was less than 25 μm. When the fine particle content was kept constant，the permeability coefficient growth rate of the tailings samples increased as the ultrasound frequency increased. When the ultrasonic frequency was kept constant，the tailings samples' permeability coefficient growth rate with higher fine particles was higher. After the ultrasound treatment，the proportion of 0-10 μm pores in the tailings sample decreased，and the decrease became larger with the increase in frequency. There was no clear trend for the proportion of pores between 10-20 μm，while the proportion between 20-40 μm increased，and the increase became larger with the increase of frequency. The proportion of pores larger than 40 μm increased relatively small. For tailings with different pore proportions，appropriate ultrasound signals of corresponding frequencies can significantly enhance the permeability enhancement effect.

In order to solve the problems of inaccurate failure priority ranking and excessively subjective risk evaluation in traditional FMEA，a risk evaluation method of oil and gas pipeline failure modes based on a hybrid weights-cloud model was proposed. Firstly，the failure modes and causes of oil and gas pipelines were investigated and analyzed，and the systematic FMEA mode was constructed. Then an expert evaluation opinion processing method was proposed based on the cloud model and the hybrid weight method. The game theory was introduced to reduce the subjectivity and randomness of the evaluation results. Finally，VIKOR was used to rank the risk of each failure mode. The feasibility and accuracy of this method was verified by comparing with the traditional FMEA method. The results show that the method can comprehensively assess the risk factors of pipeline sections，quantify the risk values of different pipeline sections and identify the most important failure modes of the same pipeline section，realizing the effective positioning of pipeline high-risk areas and the ranking of the risk factors of pipeline sections.

To explore the multi-departmental emergency response cooperation relationship of geological disasters in China，geological disaster emergency management plans at the provincial，municipal，and county levels were obtained from a geological disaster-prone province. Focusing on functional departments involved in geological disaster emergency response，a provincial-municipal-county level geological disaster emergency cooperation network model was proposed based on the social network analysis method. The theme block matrix was calculated by the centrality and iterative correlation convergence method (CONCOR) iterating correlation matrix to divide the structural hierarchy of sub-groups among functional departments. The density matrix was used to investigate the relationship patterns among subgroups and analyze the centrality，cohesiveness，and interaction of emergency departments at all levels in the geological disaster emergency cooperation network. The results showed that emergency departments had different interdependencies and small groups in geological disaster emergency cooperation，and the cooperation pattern had issues such as uneven coordination and blocked information resources. Generally，the relationship between provincial and municipal emergency cooperation was relatively close，but the overall structure and relationship pattern of the county-level emergency cooperation network were relatively isolated with a low level of connection between some organizational structures. Therefore，it is necessary to further strengthen the coordination ability among various functional departments and improve the organizational structure construction of county-level government in emergency management.

To clarify the accident causes and their association rules of strike accidents in hydropower project construction，specific accident prevention strategies were proposed to curb accidents at source. Based the selected object strike accidents during hydropower construction process，14 accident causal parameters were summarized using text mining from four perspectives including worker，machine，environment and management. Moreover，an importance analysis model was proposed by coupling association rules and the DEMATEL approach to analyze the interrelated relationship between causes and determine major accident causes. The results showed that the accident causes of object strikes in hydropower projects were closely related to each other，among which inadequate safety supervision and inspection，illegal operations，insufficient safety protection，and weak safety awareness directly affected the accident's occurrence. Furthermore，the deeper causing factors of accidents were incomplete on-site cleaning，insufficient disclosure of safety technology，and inadequate safety education. Weak safety awareness ranked first in terms of centrality and had the greatest influence on the accident causation system，thereby the above causative factors should be emphasized in project construction.

In order to mitigate the catastrophic consequences resulting from various accidents，F-N curve method was employed to conduct research on societal risk acceptance criteria for typical ships in the Yangtze River. Combined with the actual data of the bulk carriers on Yangtze River，the sensitivity analysis of the key parameters that determine the region boundary of ALARP was carried out，including EV，the risk aversion factor and the upper limit of the fatalities in one accident. For the bulk carriers，the general societal risk acceptance criterion was established. Considering the society's risk aversion to a large number of deaths，an improved acceptance criterion was proposed. Furthermore，the historical accident data of bulk carriers in the Yangtze River from 2010 to 2019 were collected. Through data analysis and frequency calculation，the societal risk of the bulk carriers caused by collision，grounding，contact，stranding，sinking，fire/explosion and wind disaster were determined，and risk assessment was conducted using the acceptance criteria established in this paper. It is shown that the risk aversion factor is the most crucial parameter to determine whether the criterion is strict enough. The number of fatalities corresponding to the horizontal intercept of the upper boundary line of ALARP region for the improved criterion is 9.55% of that of the general criterion，while considering the horizontal intercept of the lower boundary line of ALARP region，the percentage is 44.44%，which means that the improved criterion is more stringent and exhibits stronger aversion to a larger number of deaths.

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.

To address the safety management issues of oil depots in case of earthquakes，firstly，seismic resilience is defined as the ability of oil depots to maintain their storage functions in terms of resisting earthquake damage，mitigating secondary disasters，adapting to environments after earthquakes，and quickly recovering after earthquakes. Moreover，an assessment model for the seismic resilience of Oil depot is proposed by quantifying the four types of abilities. Secondly，a quantitative assessment of the seismic resilience of the oil depot is realized through a dynamic Monte Carlo method，considering the uncertainty of the resistance and mitigation stages，thereby obtaining the seismic resilience value. Finally，the performance of the proposed model is validated against the oil depot in an earthquake area. The results indicate that the uncertainty of the resilience ability of the oil depot results in a significant fluctuation in resilience values，and the domino effect has a significant influence on the oil depot's resilience and cannot be ignored. Moreover，the farther the earthquake source distance and the lower the fault slip rate lead to higher seismic resilience. The seismic resilience can be improved by staying away from active faults，predicting earthquake hazards，and reducing emergency response time.

To assess the labor intensity of construction personnel in ultra-high-altitude tunnels，this study employed theoretical analysis to define evaluation indicators and used wearable devices to monitor physiological changes during various construction phases in a specific ultra-high-altitude tunnel. Subsequently，labor intensity and efficiency of different tasks were calculated. Results reveal that the physiological indicators of different tasks are significantly different. Specifically，cardiovascular loads of the workers installing secondary lining steel bars exceed the hygienic limits，and peripheral oxygen saturation (SpO₂) levels of the mechanical operators is lower than the standard values. Labor intensity is classified as Grade V for installing secondary lining steel bars，Grade III for bottom plate construction and arch steel bar binding，and Grade II for installing secondary lining waterproof cloth and for mechanical operations. At an altitude of 4 700 meters，the labor efficiency ranges only from 76% to 88% compared to the plain areas. To match the workload in the plain areas，the labor input must be increased by approximately 13.63% to 31.58% depending on the process.

In order to improve the service level of emergency refuge in the process of disaster management，from the perspective of flood disaster，four indexes were selected: risk of disaster causing factor，sensitivity of disaster-bearing environment，vulnerability of carrier and ability of disaster prevention and reduction. The spatial distribution of flood risk was identified through the AHP-entropy weight method model. Its risk value was introduced as a weight to minimize uncovered risk. Combined with minimizing the weighted evacuation distance and minimizing the accessibility difference，a multi-target shelter location selection model was constructed from the perspective of fairness，efficiency and balance. The improved NSGA-II algorithm was used to solve the model，and the existing shelter layout in Xianyou County，Putian City was used as an example for model verification. The results show that compared with the existing shelter layout，the total evacuation distance，uncovered risk value and accessibility difference coefficient in the optimized layout scheme are reduced by 35.90%，61.47% and 33.62% respectively.

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.