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.

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.

With the rapid development of urban rail transit in China, a large amount of muck is generated during shield tunneling, and its transportation and landfill will cause serious environmental issues. Based on the Suzhou Metro Line 8 section 6, full life cycle assessment method was used to analyze and compare the carbon emissions of conventional disposal and resource utilization of shield muck. Research results indicate that the total amount of muck produced during shield tunneling is 2.64×10⁵ m³ in this project. When traditional burial treatment is carried out for shield muck, the carbon emissions throughout its entire life cycle will reach 3.00×10⁶ carbon emission equivalents. In this case, 5.28×10⁴ m² of land with a 5-meter landfill depth will be occupied by shield muck. If shield muck and industry-byproducts are used for preparing synchronous grouting materials, the 28-day strength of the grouting material can reach 2.5 MPa. Grouting raw material cost by 467 thousand yuan/km will be reduced, and about 4.6×10⁵ carbon emission equivalents per kilometer will be reduced. The findings provide reference for optimizing the resource utilization of shield muck.

To quantitatively evaluate the protective effect of the mangrove wave dissipating zone on the near coastline, based on the demonstration area of the Taishan Town Bay Mangrove Project in Guangdong Province, a combination method of on-site observation, theoretical analysis, model experiments, and numerical analysis was used to study the interaction process between the mangrove forests and waves. The verification and analysis of the wave dissipating characteristics of the mangrove forests were carried out from the perspectives of directional statistical characteristics and annual extreme value statistical characteristics, thus achieving a summary of the wave dissipating mechanism and statistical characteristics of the wave dissipating effect of the mangrove forests. The research results indicate that the basic theoretical system of vegetation wave dissipation based on the rigid column group flow theory has strong applicability to mangrove vegetation types, and the relevant wave dissipation theories can be adapted and integrated with numerical calculation models based on wave energy spectra. The simulation of vegetation wave dissipation process was carried out using physical model experiments, and the results were compared with the numerical simulation results using vegetation wave dissipation empirical formula, fully demonstrating the reliability of the vegetation wave dissipation empirical formula adopted in this study. The numerical simulation results show that mangrove forests can significantly reduce the frequency of large waves in various directions from a statistical perspective. The wave dissipation effect of mangrove forests increases significantly with the increase of wave height in different recurrence periods in the direction of strong waves. In the case of inconsistent normal wave direction and strong wave direction, the wave dissipation rate does not show a significant increase trend with the increase of wave height in different recurrence periods.

The soil covering of urban pipe jacking is shallow, and the surrounding pipelines are densely distributed and close to pipe jacking. In the process of pipe jacking construction, various factors may lead to gas pipeline fracture and gas leakage, resulting in gas, mainly gas, spreading in the soil and entering the pipe jacking, which brings potential great harm to the safety of pipe jacking construction. Based on the main-pipeline project of Maojiawan sewage treatment plant, the diffusion mechanism of gas in soil and pipe jacking and the construction ventilation technology were studied by theoretical analysis and numerical calculation. The results show as follows. With the increase of time, the diffusion flux of gas in soil first increases and then becomes stable. The farther away the pipe jacking is from the leakage hole, the lower the gas emission amount on the face, and the longer the time required for gas diffusion to the pipe jacking. The gas concentration on the opposite side of the air duct near the face of the pipe jacking is relatively high, and the gas is easy to gather. With the increase of air supply volume in the air duct, strong air flow is generated near the face, and the gas concentration near the face continues to decrease, but the rate of gas concentration reduction decreases. When the air supply volume in the air duct is 131 m³/min, the highest gas concentration in the pipe jacking is 4.57%, which is lower than the lower limit of gas explosion. With the increase of the distance between the air duct tuyere and the face, the maximum gas concentration in the pipe jacking first decreases and then increases. When the distance between the air duct tuyere and the face is 4 m, the maximum gas concentration in the pipe jacking reaches the minimum value of 4.41%. Based on this, a more reasonable ventilation parameter for pipe jacking construction is proposed.

The catenary system, which is regarded as a critical component of the high-speed rail traction power supply system, is deemed essential for the normal operation of high-speed trains. It has been demonstrated by previous earthquake disasters that the catenary system is susceptible to varying degrees of damage under seismic effects. The seismic research progress of the catenary system was systematically reviewed from four aspects: the dynamics modeling and inherent dynamic characteristics of the catenary system, the seismic damage characteristics and common types of failures, the seismic response of the catenary system and its influencing factors, and an overview of the current state of earthquake resistance research, which includes a comparative analysis of the seismic design standards and regulations for catenary systems in different countries and regions. By summarizing the relevant research, prospects for future research directions are provided.

In order to improve the accuracy of subtle fault identification, an artificial intelligence subtle fault prediction method based on the seismic data was developed. By making sample labels based on subtle fault interpretation results, a sample label library based on interpretation results was built. The subtle fault modeling and forward methods for subtle faults were developed, and a label library based on model forward was built. The special neural network for identifying subtle faults was developed, which can directly generate attribute data for subtle faults. In addition, the seismic preprocessing approaches such as removing strong seismic events and structure oriented smoothing filtering were added to improve the original seismic data. Multi-attribute fusion based on principal component was used to reflect multi-scale faults. Finally, the prediction results were verified through three steps, forming the subtle fault prediction workflow with artificial intelligence. This study demonstrates good application in the Daniudi gas field in the Ordos Basin. Compared with conventional attributes, the number of subtle fault identification has increased by 30%, and the resolution and continuity of the subtle faults have significantly improved. The prediction results are consistent with seismic data, well data, and conventional seismic attributes. Based on the subtle faults identification, the new understandings of regional structure are revealed: the dominant orientation of subtle faults is northwest, the most subtle faults are relatively concentrated in the western and northeastern parts of the survey, and the fractures between faults are also very dense. Some subtle faults are distributed along the boundary of the high-quality reservoir, perhaps related to the development of reservoirs. The prediction results contribute to evaluating high-quality reservoirs in the Lower Paleozoic and well deployment, and have application prospects for similar areas.

The stepped frequency ground penetrating radar has the advantages of high sensitivity, large dynamic range, and high average power. Radio frequency system on chip(RFSOC) based stepped frequency ground penetrating radar transceiver system design was designed and implemented. The system mainly includes modules such as the transmission link, reception link, clock unit, field programmable gate array(FPGA) control system, and data transmission unit. By setting multiple numerically controlled oscillator (NCO) for synchronization between the transmission and reception ends, the carrier was synchronized on the same frequency and phase, ensuring the phase coherence of radar transmission and reception signals. A receiving time window was also designed in the data transmission unit. The experimental test results show that the system can achieve the transmission and reception of step frequency signals with a frequency range of 200 MHz~2 GHz and a step size of 2 MHz, and can effectively detect multiple scene targets such as free space, sand pits, and asphalt pavements.

Forest fires pose a significant threat to human lives and property. Accurate prediction of forest fire risk is crucial for disaster mitigation and prevention. Influenced by factors such as terrain, meteorology, vegetation cover, and human activities, the causes of forest fires exhibit regional differences. This study uses historical forest fire events in Muli County, Sichuan Province as the response variable, with terrain, meteorological data, vegetation cover, and human activity data as explanatory variables. Leveraging CatBoost's strengths in handling high-dimensional sparse data and classification problems, a high-precision forest fire prediction model was constructed based on CatBoost. The experimental results indicate that, compared to random forest (RF), extreme gradient boosting(XGBoost), and gradient boosting decision trees(GBDT) models, the CatBoost model achieves higher modeling accuracy and significantly improves forest fire prediction accuracy, with a prediction accuracy rate of 91.36% and an area under curve(AUC) value of 0.970. Predictions made using this model can provide valuable references for the early prevention of forest fires in Muli County.

The decomposition of submerged plants such as Potamogeton crispus releases a large amount of nutrients into the water body, which has a negative impact on aquatic ecosystems. To investigate the slowing effect of filter feeding benthic animal, the Hyriopsis cumingii, on the deterioration of water quality after the decomposition of submerged plants, a 45 day experimental chamber simulation experiment was conducted from May to July 2023 using different specifications and densities of clams and seagrass combinations to monitor changes in water quality indicators and phytoplankton community structure. It was found that filtration through the Hyriopsis cumingii can reduce the total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and chlorophyll a (CHLA) in the water to a certain extent. To investigate the density effect and size effect of its Hyriopsis cumingii, low, medium, and high-density triangular sail clams were released. The experimental results show that different sizes and densities of Hyriopsis cumingii can significantly control the biomass of phytoplankton, and Hyriopsis cumingii have a significant impact on the community structure of phytoplankton. Among them, releasing small-sized (shell length 4 cm) low-density (1 g/L) Hyriopsis cumingii has the best effect on improving water quality and controlling phytoplankton biomass.