In order to explore the theoretical contribution and application value of gray rhino risk in the field of work safety. The connotation and extension of gray rhino theory were integrated to carry out the multi-perspective integration and thinking paradigm breakthrough of the three lines of defense research on work safety. The analysis of the connotation of work safety of gray rhino and the construction of prevention and control system were carried out according to the three parts of concept integration path analysis, scenario evolution chain deduction and three lines of defense system construction. The results show that gray rhino risk has very similar cognitive interpretation attributes and conceptual mapping relationships with work safety hazard, hidden danger and accident. The work safety of the gray rhino is influenced by both subjective construction and objective real risk factors. The accident scenario evolution chain is composed of the scenario components of "system energy-inducing-disaster-bearing performance" and the risk evolution stages of "accumulation-fluctuation-mutation". Three lines of defense for work safety are constructed from the prevention and control domain of "cognitive domain- prevention domain- control domain" and the hierarchy structure of "protection layer- governance layer- emergency layer". It provides the gray rhino theory and the risk metaphor method guidance for the study of work safety management.

To prevent slope instability accidents, a combined model based on HEOA optimized XGBoost was proposed to predict slope stability in response to the uncertainty of slope instability and the complexity of influencing factors. First, the main controlling factors affecting slope instability were analyzed. Six key influencing factors related to slope rock mass were selected to establish a slope stability prediction index system. Second, range normalization was applied to unify the feature scales, and SMOTE was employed to balance the distribution of stability classes within the dataset. Third, the HEOA was used to optimize the maximum depth, learning rate, subsample ratio, column sample ratio, and minimum loss of the XGBoost model. Finally, the prediction results of the constructed model were comprehensively evaluated using the following metrics: accuracy, precision, recall, F₁ score, and Cohen's Kappa coefficient, and the model was applied to specific engineering cases. The results show that the XGBoost model optimized by HEOA achieves the best performance when the maximum depth, learning rate, subsample ratio, column sample ratio, and minimum loss were 6, 0.583 8, 0.461 5, 0.584 6 and 0.024 4, respectively. Compared with other intelligent algorithms-optimized XGBoost models and single XGBoost model, the HEOA-XGBoost hybrid model shows improvements in all evaluation indicators in predicting slope stability, indicating that the model has high accuracy and generalization ability in predicting slope stability.

To accurately assess the impact of noise on the health of workers in fully mechanized mining face, the key influencing factors of noise occupational health were determined by using the theory of man-machine-environment-management system, combined with Fisher Score, maximum information coefficient and approximate Markov blanket method. The prediction model of coal mine noise risk classification based on the GBDT algorithm was constructed, and the Kappa coefficient and its accuracy were used as the index of model efficiency to compare and verify the accuracy of the model. The results show that the occupational health damage of noise in fully mechanized mining face is closely related to individual status, equipment configuration, environmental factors and occupational health management. Among these, job category, individual age, length of service, protection awareness, degree of equipment automation, pass rate of noise monitoring points, noise exposure, reverberation time and management institutions and personnel are the key indicators of occupational health risk classification and prediction. The accuracy of the occupation health risk classification prediction model of coal mine noise based on GBDT is up to 99.6%, and the average accuracy and Kappa coefficient are 98.3% and 0.958, respectively. The evaluation accuracy of six prediction models for noise occupational health risk classification in fully mechanized mining face is ranked as follows: GBDT > Genetic Algorithm optimization Random Forest(GA-RF) > Particle Swarm optimization Least Squares Support Vector Machine(PSO-LSSVM) > Random Forest(RF) > Support Vector Machine(SVM) > Decision tree.

In order to optimize engineering fire-extinguishing technologies, numerical simulation was employed to investigate the effects of flow rate and pipe diameter at the foam inlet on the transport behavior of compressed air foam. In the simulation, the flow rate varied within the range of 1 000-2 400 L/min, and the pipe diameter ranged from DN80 to DN220. Characteristic parameters characterizing foam transport were extracted, including flow field, vorticity, viscosity, and pressure drop. The results show that the velocity of the foam fluid at the elbow forms three regions from the outside to the inside: low-speed, high-speed, and low-speed. However, the flow rate and pipe diameter changes have different degrees of influence on these three regions. Regarding the effect of inlet flow rate, there is a positive correlation between vorticity and inlet flow rate. Additionally, the low-speed region on the inner side of the elbow increases, the high-speed region is squeezed toward the outer wall, and the range of the low-speed region on the outer side decreases. Meanwhile, as the inlet flow rate of the foam fluid increases, the viscosity of the fluid gradually decreases, and the pressure drop first increases and then tends to stabilize. In terms of the influence of pipe diameter, compared with the inlet flow rate, the changes in velocity stratification and vortex near the elbow are relatively slight when the pipe diameter increases. Moreover, the pressure drop inside the pipe gradually decreases with the increase in pipe diameter. When the pipe diameter increases from DN80 to DN220, the local pressure drops decreases by approximately two-thirds.

In order to optimize the design of fire-fighting equipment, the influence of apertures on the performance of compressed air foam jets was investigated through experiments involving various jet apertures and gas-liquid ratios. The data, including the expansion ratio, liquid drainage time, foam jet range, and width, were collected. It was found that as the jet aperture decreased, the deviation of the actual expansion ratio from the ideal value increases, and the deviation increases as the gas-liquid ratio increases. The minimum jet aperture threshold meeting the stability requirements of the compressed air foam system is determined. The critical gas-liquid ratio for a stable foam jet decreases as the aperture diminishes, and a relationship between the jet aperture and the critical gas-liquid ratio is established. The effect of jet aperture on the diameter of foam D₃₂ and 25% drainage time is not obvious. For gas-liquid ratios below 20, in the same gas-liquid ratio, the jet aperture is inversely proportional to the range and width of the foam. Under the same jet aperture, beyond a certain gas-liquid ratio, the variation in foam range becomes negligible. Additionally, the underlying causes of the observed critical gas-liquid ratio, 25% drainage time, and variations in range and width are analyzed.

In order to ensure the safe operation of gas pipeline networks and to enable the overall reliability assessment of pipeline systems, a three-level reliability evaluation model "component-segment-pipeline network" was proposed for natural gas pipeline networks. First, the state equations of steel pipe and weld units under different failure modes were established by analyzing the composition of the pipe network line system. Combined with the Monte Carlo algorithm, the failure probability under different failure modes was calculated. Based on fault tree analysis and the minimal cut set theory, a "component-segment" level reliability model was developed, enabling the hierarchical reliability evaluation from components to pipeline segments. Next, the GO method was applied to analyze the topological structure of the pipeline network, and a structural reliability model at "segment-pipeline networ" level was established. The structural reliability of the entire pipeline network was calculated and weak segments in terms of reliability were identified rapidly. Finally, a case study was conducted to verify the accuracy and applicability of the proposed model. The results show that the reliability of pipeline network systems is positively correlated with pipe material strength and wall thickness, and negatively correlated with the diameter-to-thickness ratio and internal pressure. Compared to high-grade pipelines represented by X80, networks composed of lower-grade pipelines exhibit higher sensitivity to external factors, with their reliability being more significantly affected by external disturbances.

In order to address the problem of low payload and spatial utilization rates in the air transportation of emergency supplies, this study focuses on mixed loading schemes for transport aircraft, targeting non-standard emergency materials characterized by diverse types, significant variations in size and mass, and the presence of bundling or proportional transportation requirements. A size-based classification criterion was proposed to divide the materials into three categories: large, medium, and small. For medium and small items, a multi-objective two-dimensional loading model was established and solved by integrating the bottom-left algorithm with NSGA-III. The resulting configurations were then treated as large-sized items. Within the aircraft cargo hold, NSGA-III was further employed to generate loading schemes for these large-sized items, with the objectives of maximizing loaded area and payload weight while minimizing center-of-gravity deviation. The results indicate that, through material classification and staged optimization, the proposed method effectively reduces the dimensionality of the solution space and improves computational efficiency. The generated loading schemes achieve an average cargo space utilization rate of 78.09%, an average payload utilization rate of 86.19%, and an average center-of-gravity deviation of only 0.222 meters. This approach significantly enhances the utilization of transport aircraft while ensuring flight safety, and provides timely decision-making support for emergency relief operations.

In order to improve the accuracy and reliability of ship-bridge collision risk assessment for navigable bridges, the causes of 248 ship-bridge collision accidents at home and abroad were statistically analyzed and identified, thereby constructing the initial index system of ship-bridge collision risk factors, including 23 risk causes. Based on the fault tree analysis method, the initial fault tree of ship-bridge collision risk was constructed. Boolean algebra operation was used to obtain the fault tree cut sets. By merging and deduplicating the minimum cut sets, the number of causes in the initial fault tree was first simplified. According to the calculation and analysis of the accident probability caused by residual risk causes, combined with Pearson correlation analysis of risk factors, the number of fault tree risk causes was simplified in the second step, and then the main risk factors affecting ship-bridge collisions were identified. The results show that factors such as "navigational conditions and exceeding navigational clearance height", "poor working status", "weak communication ability and poor crew ability", "ship equipment aging and psychological habit" should be incorporated into risk causes including "unfamiliarity with navigational conditions", "lack of navigation technology", "mechanical equipment failure and fluky psychology". Abnormal psychology did not cause accidents during the 8-year period of accident probability analysis, and its proportion in accidents over 10 years was only 0.21%, so it should be eliminated. The correlation between ship density, management of navigational aids and other factors is weak, and the probability caused by the two factors is low, so they can be removed from the main risk factors of ship-bridge collision. Therefore, a ship-bridge collision risk evaluation index system for navigable bridges is constructed, which is based on 14 main risk factors such as meteorological and hydrological conditions, mechanical equipment failure, and weak professional navigation technology and weak emergency response capability.

To address the issues of preference dependence and loss aversion of decision makers under risk conditions in the evaluation of urban flood disaster emergency management efficiency, an improved grey group decision-making model integrating the risk preference of decision-makers was constructed. Firstly, a decision-maker weight determination method based on group consensus and difference was proposed, which overcame the limitation of traditional method that ignored differences of decision-maker's professional background, experience and personal preference when integrating individual information. Then, the TODIM method was proposed to reduce the problem of loss overestimation faced by decision makers under risk when calculating dominance. Finally, the model was applied to the efficiency evaluation of urban flood disaster emergency management schemes, which verified its effectiveness. The results show that the scheme of improving the emergency response and rescue system is superior to both the early warning and monitoring system construction scheme and information technology support scheme, when comprehensively considering emergency rescue efficiency, disaster prevention and mitigation effect, resource utilization efficiency and emergency management cost. Moreover, this optimal emergency scheme remains robust in 81.82% of the scenarios.

To improve the efficiency of safety inspections for existing civil buildings, this study develops a decision-support model for safety evaluation of existing civil buildings based on ML techniques. Building safety feature data were first collected from building evaluation reports. Then, a multi-dimensional indicator system integrating "design-evolution-status" features was established through correlation analysis and recursive feature elimination with cross-validation. Subsequently, five ML models were built and evaluated using performance metrics such as accuracy, precision, and recall. Furthermore, a decision-support platform for safety evaluation of existing civil buildings was developed and validated through a real engineering project to examine its practical operability. The results demonstrate that, compared to design features, evolution and status features more effectively reflect the actual safety conditions of civil buildings. In particular, building age, renovation or extension history, and concrete beam load capacity are identified as key features. Among the tested models, the Decision Tree algorithm shows the best performance in evaluating the safety of enclosure system, superstructure, foundation, and individual evaluation unit.