The flow pattern and its characteristics in the flood discharge stilling basin with step-down floors play dominant roles in effectively dissipating the discharged flooding water energy, guaranteeing the safety of the hydraulic structure and its downstream river bank stability as well as navigable flow conditions. A three-dimensional numerical model for water-air two-phase flows with strong nonlinearity, involving large free surface deformation and complicated solid-wall boundary conditions was established, and was applied to analyze flow characteristics in Xiangjiaba flood discharge stilling Basin with step-down floors. RANS(Reynolds-averaged Navier-Stokes) equations, Realizable k-ε turbulence model and VOF free surface tracking method were used in the developed numerical model. The model was firstly validated through comparisons of the simulated results and measured data for the flow patterns as well as the time-averaged pressure at the dam surface induced by the flood discharge jet. It is then applied to simulate the 3-D flow structure and fluctuation characteristics in the stilling basin with step-down floors induced by flood discharge from dam under the same discharge amount but different flood discharge scenarios. The results show that, for the flood discharge stilling basin with high and low step-down floors,under the same flood discharge condition, different scenarios of dam discharge gate open mode significantly affects the three-dimensional structure and characteristics of discharged flow in the dissipative pool induced by flood jets. The combined discharge from dam surface and middle holes results in the strong turbulent mixing of submerged multi-layer and multi-jet flows, effectively dissipating the energy of the discharged water and reducing the water surface fluctuations. The established numerical model can better reproduce the high-speed flooding jets and its turbulent motion in the dissipative pool associated with flood water discharge.

Bolts are the key to the stable connection of high-altitude equipment, but they are prone to abnormalities such as loosening under the influence of various factors, threatening the safety of the equipment. Currently, bolt detection methods based on deep learning are faced with the problems of class imbalance and label missing. Existing deep-learning-based bolt detection methods suffer from class imbalance and missing labels. A HDWL(historical dynamic weighted loss) model based on semi-supervised pseudo-label learning was proposed. By dynamic weighted orthogonality and class-adaptive fair punishment, the model classification was evaluated with historical data. Adaptive punishment was introduced to prevent overfitting and focus more on hard-to-classify samples, boosting model performance. Experiments showed that the HDWL model achieved significantly higher accuracy than other methods, with advantages in minority-class training and feature focus.

In order to solve constrained optimization problems, a dingo optimization algorithm with ε constrained method and crisscross strategy (εCDOA) was proposed. The algorithm first introduces the crisscross strategy in dingo optimization algorithm after all individuals select one hunting strategy and update their positions, so as to improve the global and local search capabilities of the obtained algorithm, which also help the algorithm jump out of the local optimum. Then, according to ε constrained method, the equality constraints were transformed into the inequality constraints, and the ε level comparison method was used instead of fitness value comparison to evaluate the qualities of the dingoes. Finally, based on the individuals’ constraint violations, the population is divided into two subgroups according to adaptive ε values. The individuals’ survival rates were calculated using the survival strategy of each subgroup, and the individuals with low survival rates were updated. The results of numerical experiments on 19 standard constrained optimization problems in CEC 2006 show that algorithm εCDOA has better optimization performance than four comparative algorithms such as dingo optimization algorithm with ε constrained method. For three classical engineering design problems, the design schemes given by algorithm εCDOA are obviously better than those given by other algorithms.

For the group decision-making based on generalized grey target with mixed attributes, a group information aggregation method based on an improved power-weighted averaging operator was proposed. This method takes into account the interrelationships among decision-makers and among attributes, reducing the distortion of uncertain information and simplifying the computational process. First, the mixed attribute data are uniformly measured and transformed. Then, the comprehensive weighted G-S(Gini-Simpson) index was calculated by evaluating each expert’s values relative to the target centers of all experts, to determine the objective weight of experts. Next, the differences among experts are further calculated by the comprehensive weighted G-S index. Finally, a novel information aggregation method was constructed based on the proposed improved power-weighted averaging operator to aggregate group decision-making information with mixed attributes. The effectiveness and feasibility of the proposed method are verified by a case analysis.

A critical human factors analysis method for aircraft runway overrun and veer-off event was proposed to address the complex causal relationship of human factors that have not been fully revealed in the investigation of the event. Firstly, improve the human factors analysis and classification system model to identify the root cause of overrun and excursion event, and use the fault tree analysis method to identify the direct cause of runway overrun and veer-off event. Secondly, a human factors investigation decision support model was constructed, and obtained the causal chain of aircraft runway overrun and veer-off event. Then, a Bayesian network was constructed to quantify the importance of the causal factors for the runway overrun and veer-off event caused by human factors. Through predictive reasoning, diagnostic reasoning, and sensitivity analysis, the key causal chain of the runway overrun and veer-off event was obtained. The results indicate that 32 causal chains of overrun and veer-off events is obtained based on the proposed human factors investigation decision support model, comprehensively revealing the human factors and their coupling relationships of the event. Bayesian inference can obtain the key causal chain of “improper action execution/improper information preprocessing → unsafe behavior → runway overrun and veer-off”. The above conclusions are basically consistent with the findings of investigations into aircraft runway overrun and veer-off events, and they hold positive significance for improving the early warning and prevention capabilities of such incidents.

The voltage fluctuations in the high-voltage DC bus of the train traction converter have a significant impact on the output power quality of the traction system. Therefore, it is necessary to improve the response speed of the intermediate stage isolated DC/DC converter to reduce the power coupling between the high-voltage stage and the low-voltage stage. Taking the isolated DC/DC converter as the research object, an unbiased model predictive control and sampling noise suppression strategy was proposed to address its inherent problems of high sensitivity to circuit parameters and susceptibility to sampling noise. Firstly, the operation principle of the dual-bridge series resonant converter and the causes of the steady-state errors were analyzed, and a feedback correction method based on recursive least square algorithm was designed to eliminate the steady-state error. Then, the introduction of noise suppression coefficient reduces the sensitivity of the control variable to the control target through a simple and effective method. Furthermore, the virtual current was utilized in predictive model instead of the actual current sampling value, and it further reduces the system costs. Finally, an experimental platform was built to verify the improvement of the proposed strategy in both steady-state and dynamic performance.

Based on the actual needs of large drilling spacing, and frequent long-distance relocation of drilling rigs in coal mine underground construction, as well as the need for remote control function, a research idea of compact layout and modular design of each unit of rubber wheel directional drilling rig was adopted to solve key technical development problems, such as independent walking rubber wheel chassis, multi power output units, hydraulic system, and electrical control system. After whole machine was processed and assembled, load testing was simulated on the drilling performance test bench of the National Safety Production Inspection Center. The experiment shows that all functional parameters of the drilling rig meet the design requirements. The ZDY3500JDK rubber tyre directional drilling rig developed for coal mine meets demand for long-distance autonomous relocation underground, greatly improving the production and transportation efficiency of mine, and providing reliable equipment support for drilling construction operations in large and medium-sized mines with trackless rubber tyre transportation.

The external human-machine interface is used to enhance communication between autonomous vehicles and road users like pedestrians and cyclists, thus traffic safety and user experience are improved. The recognizability of the external interface is considered the foundation for ensuring effective and understandable signal functions, and it is explored to ensure pedestrians crossing safety. The form of information expression, interface location, and the speed of autonomous vehicles were considered as independent variables, and eye-tracking technology was used to collect eye movement and behavioral data. The identifiability of the interface was evaluated through repeated measures analysis of variance and logistic regression. The results show that the form of interface information, location, and vehicle speed significantly affect identifiability. The light band has the best identifiability; higher recognition efficiency is observed when the vehicle is traveling at a low speed; and the highest recognition efficiency is found when the interface is at P3, while the lowest is noted at P1 and P4.From the perspective of enhancing pedestrian traffic safety, this study provides a reference for the design of the external interface of autonomous vehicle while driving, and helps to improve pedestrian attention and recognition accuracy of the external interface.

To address the issues of distortion and poor segmentation results in weld defect image segmentation, the crack and porosity welding defect images in the rim production process were taken as the research object. An improved particle swarm optimization algorithm based on simulated annealing is proposed for the three-threshold image segmentation of welding defects. First, a three-dimensional Otsu model is constructed using the grayscale value, average grayscale value, and median grayscale value of the image. Next, an adaptive inertia weight and asymmetric learning factor were introduced and integrated into the SA strategy to enhance the algorithm’s solving efficiency and ability to escape local optima. Finally, the SA-IPSO algorithm was used to optimize the three-dimensional Otsu model to obtain the optimal threshold and corresponding defect segmentation image. Various algorithms and models are employed to segment welding defect images. The results show that for crack and porosity defect images, the proposed improved algorithm outperforms the comparison algorithms in terms of peak signal-to-noise ratio and structural similarity evaluation metrics. The proposed method accelerates algorithm convergence while preventing distortion in segmentation results, thereby improving segmentation accuracy.

Pipeline leakage is a major cause of urban road collapse accidents. Understanding the evolution process and catastrophic mechanisms of road subsidence is crucial for preventing such safety incidents. Focusing on sewage pipelines in Beijing municipal roads, this study employs DEM-CFD(discrete element method-computational fluid dynamics) coupled flow-solid approach. Microscopic model parameters were calibrated based on laboratory experiments to simulate deformation and cavity evolution in sandy soil layers under various pipeline leakage locations and burial depths. Key parameters, including particle displacement, soil compactness, and medium flow, were analyzed during cavity formation. The results indicate that leakage at the top and middle of the pipeline leads to the formation of a funnel-shaped cavity as water and soil are lost. Without traffic load, the road surface exhibits negligible settlement. By analyzing particle displacement and compactness variations, the soil deformation was divided into stable, loose, and cavity zones, and an elliptical partition model was established for the loose zone. Based on the particle loss rate, the progressive failure process of the soil was classified into three stages: particle migration, rapid loss, and gradual convergence. In terms of cavity formation time, subsidence extent, particle loss rate, and total particle loss, leakage at the pipeline’s middle section yielded the highest values, followed by the top section, with the lowest at the bottom section. However, bottom leakage resulted in the largest loose zone. These findings provide theoretical support for detecting and identifying underground risks associated with urban road collapse disasters.