To study the performance of different airports' resilience under meteorological disasters and the causes of their differences，firstly，the definition of airport resilience based on airport functional level was put forward，which covered three sub characteristics: resistance，robustness and recovery. Then，by calculating the airport functional level under meteorological disasters through flight data，airport resilience index and sub characteristic indices were obtained to reflect the airport's resilience. Finally，taking the disaster of the snowstorm as an example，the distribution pattern of resilience index of affected airports in the United States and the reasons for the differences of resilience index among different airports were analyzed. Furthermore，the performance of the resilience index of the affected airports under the disaster of winter storms，floods，tropical storms and tornadoes was analyzed. The impact of disaster types on airport resilience index was also analyzed. The results indicate that the difference of airport resilience index is mainly caused by the resistance index. The key factors that cause the difference in airport resilience index under snowstorm disaster are throughput，aircraft fuselage maintenance plan level and engine maintenance level. The resilience index of the airport under winter storm，flood and snowstorm is basically the same. The lowest relative difference of the resilience index is 7.519% and 5.521%，while the average relative difference is 23.021% and 21.037%. The calculation method of airport resilience index proposed in this paper can accurately reflect the resilience properties of airports.

In order to provide reference direction for the research of emergency social mobilization more accurately，taking 296 research papers on social mobilization for emergencies included in Chinese Social Sciences Citation Index(CSSCI) journals in China National Knowledge Infrastructure(CNKI) as samples，this paper comprehensively used bibliometrics，knowledge graphs and other visual analysis methods to analyze the number，distribution，institutions，research topics，hot spots and changing trends of papers in this field over the years. The research shows that since 2003，the number of papers published in the study of social mobilization for emergency response has experienced three stages of development，with the increasingly wide distribution of periodicals and loose publishing institutions. There are more and more research topics，mainly focusing on the trigger scenario，implementation mechanism and functional effect of emergency social mobilization in the emergency response stage. Hot topics are constantly emerging with more breadth and depth，and the research methods are relatively limited. There is much room for further research and development，which can focus on the research contents of multi-methods，the whole process and intelligence of emergency social mobilization.

To fully identify the interaction and coupling effects between the subsystem elements in SPO mode，an improved FRAM was developed to propose a quantitative analysis model based on the risk evolution mechanism. Firstly，the fuzzy comprehensive evaluation method was used to evaluate the functional variability of system functional modules. Then，the concept of structural importance was introduced to analyze upstream and downstream coupling variability of functional modules of the computational system and determine the coupling effect mechanism between various functional elements of the system. Finally，the Monte Carlo simulation method was used to calculate the functional resonance risk index for SPO-specific scenarios，analyze potential functional resonance situations，and set effective functional barriers. The results showed that the improved functional resonance analysis method can explain the nonlinear coupling situation of SPO. The functional variability coupling change index of modules such as air traffic control and services，pilot cognitive state，and captain control was relatively high with a value of more than 2.5. In the approach and landing scenario，eight functions (e.g.，crew technical training，important meteorological information，air traffic control services，and ground information support) were prone to functional resonance. Combined with the functional resonance results，the physical，symbolic，functional，and invisible functional safety barrier measures were set to provide specific operational suggestions.

Aiming at the problem of competing multiple failure modes and dependent variables of corroded pipelines，and low efficiency of predicting the probability of small failures，a time-varying failure probability prediction method for corrosive pipelines based on SS method is proposed. Based on the limit state function of leakage and burst failure，considering the competition between leakage and burst failure，a prediction model for the pipeline competitive failure probability was constructed. In order to solve the above model，the Nataf transformation method was used to describe the correlation between the depth and length of the defects in the simulated samples，and a method to solve the failure probability of corroded pipelines was proposed by considering the competitiveness of failure modes and the correlation of variables. Monte Carlo Simulation (MCS) was used to verify the above method. Finally，the above method was applied to investigate the effect of the weak correlation (correlation coefficient 0-0.3) between defect depth and length on the probability of failure within 15 years of pipeline service. The results show that when the failure probability is greater than 10^-6，the calculation results of this method are basically consistent with those of MCS，and the calculation efficiency is higher than that of MCS，and the minimal probability events with the probability level of 10^-12 can be predicted. Within 15 years of pipeline service，the greater the correlation coefficient of defect variables is，the greater the probability of pipeline burst failure is，and the earlier it reaches the threshold year of burst failure probability. As the correlation coefficient increases，the smaller the probability of leakage failure is，and the later it reaches the threshold year of leakage failure probability. At the later stage of the prediction，the effect of the correlation coefficient increase on the probability of the two kinds of failures is weakened.

The causality causal graph of hazardous chemical accidents was developed to improve the safety management level of hazardous chemical enterprises. Firstly，based on the accident investigation report，an entity-relationship joint extraction model was proposed through an improved CasRel technique. Furthermore，the proposed model aimed to improve the extraction accuracy of textual information by incorporating the relationship-aware bidirectional encoder representation method (R-Bert) and Span pointer network. Subsequently，similarity calculation methods were used to generalize the events to enhance the graph's comprehensiveness and accuracy. Then，the refined data was stored in the Neo4j graph database visualizing the associations between events. Finally，the corresponding guestion-answering system was proposed based on the developed causal graph，and then an intelligent question-answering system for the causality of hazardous chemical accidents was proposed. The results indicated that the F₁ value calculated by the improved CasRel model was 90.5%，and the prediction accuracy of the proposed model was 2% higher than that simulated by the original model. The hazardous chemical accidents causal graph and intelligent question-answering system performed well in terms of multiple evaluation indexes，clearly revealing the logical relationship between events. Therefore，the proposed model in this study can meet question-answering needs of hazardous chemical accidents，facilitating the exploration of accident patterns and potential risk factors，and enabling accident trend prediction.

To solve the issues of wind turbine blades in terms of classification difficulty and blurry segmentation of small defects in surface defect detection，an improved U-Net semantic segmentation network was proposed based on dilated convolution and convolutional attention modules. Based on the encoding-decoding structure of the network model，a transferable VGG16 feature extraction layer was used to replace the encoding part of the U-Net network. Then，a convolutional attention module was added to the skip module between encoding and decoding. The global weight was enhanced by selecting small defect information. Dilated convolution was used in the decoding section to enhance the network's feature extraction ability，and the pre-trained VGG16 model was used to realize transfer learning. The hybrid loss function of Focal and Dice was validated against the models of DeeplabV3+，Pyramid Scene Parsing Network(PSPnet)，High-Resolution Network(HRNet)，and U-Net. The results showed that the improved U-Net network had higher prediction accuracy in blade defect classification and segmentation tasks，mean intersection over union，mean pixel accuray，and recall values were 83.60%，92.84%，and 88.50%，respectively. The mean intersection over union simulated by the improved U-Net model was 13.98% and 9.38% higher than that by the DeeplabV3+ and standard U-Net model，respectively. Therefore，the proposed model can improve the sensitivity of blade defect detection，effectively reduce false positives of detection results，and provide guidance to wind turbine blade defect detection.

In order to explore the coupled evolution mechanism of air traffic operation safety risk，clarify the mechanism of coupling and mutation formation in air traffic operation systems based on a combination of the N-K model and FRAM. Firstly，textual data on unsafe incidents was collected. The risk factors involved were categorized，and their historical frequency of occurrence and the coupling relationship between risk factors were obtained. Secondly，the N-K model was used to solve the coupling degree values between air traffic operational risk factors. Finally，based on the output time and accuracy，the variability of the FRAM functional module was quantitatively evaluated，analyzing the coupling mechanism of air traffic operational safety risks，and safety risk analysis was conducted using regional area navigation(RNAV)approach unsafe events and deviation route unsafe events as examples. The results indicate that the evaluation method based on improved FRAM can quantitatively calculate the variability between functional modules in a reasonable and effective manner，weaken the dependence of traditional analysis methods on subjective consciousness，and make the analysis results more objective and scientific.

In order to improve slope stability under the combined action of engineering disturbance and natural factors，and clarify the process and characteristics of slope soil nailing under different loading conditions，the optimal support mode and layout technology suitable for the actual working conditions were selected to ensure the intrinsic safety of the slope structure system. Firstly，the physical model test process and results of soil nailing support under three types of static，vibration，and centrifugal forces were systematically elaborated. Secondly，the characteristics and applicability of typical slope support technologies and new slope support methods were compared as well as analyzed. Finally，through physical model tests under complex environmental effects，the deformation and failure mechanisms of three special slopes，namely submarine slope，typhoon rainstorm slope and high-speed and long-distance landslide were explored. The results show that soil nailing has good applicability in all kinds of slope support，but the reliability and safety factor of slope support under complex and special environments still need to be improved. Therefore，in practice，it is necessary to integrate new materials and processes to conduct research on composite support structures. At the same time，it is necessary to strengthen the selection of similar materials，innovative observation methods，and special environmental simulation research in physical model experiments，aiming to improve the high degree of restoration of monitoring data and achieve the full process safety guarantee of slope systems.

In order to solve the problems of difficulty in quantifying the indicators and difficulty in taking into account randomness and fuzziness in the evaluation process of the risk of the coal mine intelligentization project，the cloud model theory was adopted to carry out a quantitative and comprehensive evaluation of the system. First of all，based on the coal mine informationization system construction project，a multi-dimensional analysis was carried out to establish a multi-indicator and multi-dimensional evaluation system of the project risk. Then，the combination of hierarchical analysis (AHP) method and criteria importance though intercrieria correlation (CRITIC) method was used to assign weights，determine the weight matrix of the indicators，and the cloud model was used to realize the conversion between the quantitative and qualitative indicators，to complete the evaluation of the risk of the coal mine intelligence project，and to put forward the targeted policy according to the evaluation results. The cloud model was used to realize the quantitative and qualitative conversion of indicators，complete the risk evaluation of the coal mine intelligentization project，and put forward targeted policies based on the evaluation results to minimize the existing risks of the project. Finally，taking a coal mine of National Energy Group as an example，the risk evaluation of the construction and implementation process of coal mine intelligentization project was carried out. The results show that the cloud model can realize the quantitative evaluation of project risks，and the results of risk evaluation coincide with the actual situation on the site; the results of risk evaluation can help to solve the hidden risks on the site and improve the ability of risk control.

In order to coordinate the distribution and use of materials in the emergency supply chain，the reliability evaluation indexes of the emergency supply chain were refined based on literature analysis. The reliability evaluation index system of the emergency supply chain was established，which was composed of three first-level indicators of organizational guarantee reliability，system institutional reliability and operation process reliability and 17 corresponding second-level indicators. AHP，EWM，and trigonometric fuzzy number were used to calculate the index weights，and the partial order relationship containing weight information was introduced into the evaluation，and the combined weighting-partial order set model was constructed to evaluate the reliability of the emergency supply chain. The reliability level of the emergency supply chain in five county-level cities of city C was evaluated and analyzed by using the model. The results show that the reliability of the emergency supply chain in the five county-level cities can be divided into four categories，among which the reliability level of region 03 is high，the reliability level of region 04 and 02 is relatively high，the reliability level of region 05 is medium，and the reliability level of region 01 is low. Through comparative analysis，the strengths and weaknesses of each region，and the areas needing improvement are obtained. This evaluation model can effectively evaluate the reliability level of the emergency supply chain.