Frame-rocking wall was a composite self-resetting structure that could effectively improve the seismic resistance and toughness of buildings. To fully understand the random response characteristics of structures under earthquakes，a simplified nonlinear equation for a multi-degree-of-freedom frame-rocking wall structure was constructed，and an equivalent linear dynamic equation with time-varying parameters was constructed based on the assumption of pseudo harmonic behavior in response using equivalent linearization. Further，based on the principle of random averaging，the Fokker-Planck-Kolmogorov(FPK) equation could be derived to determine the time evolution form of probability density function(PDF) for controlling the amplitude of the response，and ultimately the first-order differential equation for the time-dependent variance of the random response could be obtained. Finally，a computational model was constructed using a framework of a certain teaching building as a sample for validation. The results show that the approximate analytical method has excellent accuracy，and while ensuring the accuracy of the random response time-related variance，it can improve the efficiency of analysis compared to traditional Monte Carlo simulation(MCS) methods. In the results of non-steady ground motion power spectrum models in separable and non-separable forms，the trend of the random response variance curve is related to the form of random seismic excitation，and its segmentation points show obvious unsmooth phenomena under the action of segmented modulation of non-stationary spectra. The results under different types of random seismic excitation disturbances demonstrate the excellent applicability of this method.

In order to improve the performance of small target detection in infrared imaging and the ability of low altitude airspace supervision，an infrared small target detection network based on multi-scale attention feature enhancement fusion was proposed. Firstly，Resnet34 was used to extract the multi-scale features of infrared images. Secondly，the multi-scale spatial attention feature enhancement module(MFEM) was used to improve the ability of feature extraction. Then，in the step-by-step up sampling process，the dual channel attention feature fusion module(DFFM) was used to fuse the semantic information and detail information to better protect the characteristics of infrared small targets. Finally，taking the video sequence detection of ground/air infrared dim small aircraft target as an example，the real scene test was carried out by comparing with other methods. The results show that compared with existing methods，the proposed method improves the scores of intersection over union(IoU)，F-measure and false negative rate(FNR)，and can accurately locate the target and generate good segmentation results. The DFFM can simultaneously use multi-scale context information and spatial attention mechanism to highlight infiared small targets. The DFFM assigns weights to sets of different channel features，thereby obtaining the most appropriate feature map for feature fusion and improving the detection performance.

In order to solve the problem that the pressure drop signals caused by compressor suction or upstream block valve cut-off conditions leaded to incorrect shut-off of the block valve，and the problem that the block valve failure due to insignificant pipeline pressure drop caused by small hole leakage，a simulation model was established. Taking a typical gas transmission trunk line as the research object，300 sets of pressure drop signals under three different working conditions，namely compressor suction，emergency cut-off of the block valve and pipeline leakage，were obtained. The pressure drop rate of the pressure drop signal was calculated by point-to-point detection method. Singular value decomposition(SVD) method was used to extract the characteristics of the pressure drop rate signal，and the min-max normalization method was used to normalize the characteristic values of the pressure drop rate signal. SVM method was used to identify the characteristic value signals of different pressure drop rates，and the corresponding working conditions were obtained. To solve the problem that the unreasonable setting of kernel function parameters and penalty factors in the SVM model affected the accuracy of algorithm recognition，TLBO algorithm was used to optimize the kernel function parameters and penalty factors，and a TLBO-SVM model for intelligent identification of gas pipeline leakage signals was established. The model was applied to classify and identify 300 groups of simulated pressure drop rate signals in three working conditions. The results show that the recognition accuracy of the model is 92.22% for three kinds of pressure drop rate signals under different working conditions. The identification accuracy is 96.67% for small hole leakage with a leakage diameter of 50-125 mm and a pressure drop rate range of 0.01-0.07 MPa/min. For the actual leakage pressure drop rate signal of a main pipeline，the accuracy of TLBO-SVM is 100%.

In order to improve the reliability of the logistics facility network of railway construction projects in complex environments，scenario reduction techniques were used to generate a minimum subset of disruption scenarios and their disruption probabilities to describe the disruption scenarios of transport channels. The polyhedral uncertainty sets were used to describe the uncertainty of logistics demand. To minimize the combined costs of transport，construction，operation and penalty costs，a two-stage stochastic and robust optimisation technique was applied to construct an uncertainty optimisation model for the location of material reserves bases. The model was solved based on a C&CG algorithm. The validity of the model and the algorithm was verified by taking a C railway construction project in a complex environment as an example. The results show that the cost variation coefficient of the model-acquired solutions is 4.3% of the traditional model in the random disruption scenario，and the cost fluctuation of the model-acquired solutions can be up to 38% of that of the traditional model in the extreme demand fluctuation. The two-stage uncertainty optimisation model given in this paper can effectively reduce the cost variation of the logistics facility network resulting from the disruption of transport channels and demand fluctuations.

In order to improve air traffic controllers' emergency response ability，a reliability model of air traffic controllers' SA was constructed from three aspects: air traffic controllers' personal ability，air traffic control task characteristics and air traffic control equipment. Tower control simulation software and SA Global Assessment Technique (SAGAT) were used to measure the air traffic controllers' SA level in emergency situations. Based on BN，SA reliability was quantitatively analyzed to predict air traffic controllers' level of SA，and based on Bayesian inference，the key factors affecting the reliability of air traffic controllers' SA were analyzed. The results show that the reliability of SA of air traffic controllers is positively correlated with the level of SA in emergency scenarios，and the SA level can be predicted by air traffic controllers' SA reliability. The BN inference analysis reveals that the factors with a higher degree of influence and sensitivity to the reliability of controllers' SA were the deployment of aircraft availability time，the accuracy of the control equipment and the controller's memory. The causal chain that has the greatest impact on the air traffic controllers' SA reliability is the deployment of aircraft availability time→mission characteristics→SA reliability.

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.

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.

In order to solve the nonlinear problem of dynamic loss of soil around the pipe during the collapse development process of the buried pipeline，first，a numerical analysis method was used to construct a nonlinear coupling model of pipe-soil that passes through the collapse zone. Then，the model was verified based on experimental measured data and specifications. Finally，a study on the damage mechanism of buried pipelines subjected to soil collapse was carried out，and the dynamic evolution process and mechanical characteristics of buried pipeline collapse were discussed. The results show that the axial stress is the control stress，the mid-span pipe bottom is the control point，the mid-span section is the dangerous section，and excessive tensile stress is the main reason for failure of buried pipelines. It is confirmed that "pipe-soil separation" phenomenon exists. When the collapse depth reaches 48 mm，"pipe and soil separate"，as the collapse process progresses，the entire process of pipe-soil structure from the beginning of deformation to the tensile failure of buried pipeline can be divided into three stages: the top pressure stage，the transition stage and in the bottom tension stage，the collapse depth of 80 and 160 mm is the dividing point. When the collapse depth reaches 59 mm，the pipe top stress changes from valley to peak at mid-span position. When collapse depth reaches 80 mm，friction stress appears.

To effectively control the smoke spread in large flat-space ships，the oil pool mass loss rate，chamber temperature distribution，thermal insulation efficiency，and smoke control effect were investigated during large-scale fires. The experimental study was performed under three different smoke screen heights (0.35，0.55，and 0.70 m) and mechanical ventilation conditions in a chamber with dimensions of 30 m×24 m×2.3 m. The results indicated that the increase in smoke screen height caused a decrease in the peak value of the oil pool mass loss. The smoke screen height had a more significant impact on the smoke temperature in the chamber's upper layer than that of the lower layer. The peak temperature above 1.4 m was significantly decreased，whereas no significant change in the peak temperature below 1.4 m was observed. The average ceiling temperature and thermal insulation efficiency decreased with the increase in the smoke screen height. The thermal insulation efficiency increased from 28.2% to 50.8% under the combined smoke control mode，and it increased from 29.4% to 54.7% under the independent smoke control mode.

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.